diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
new file mode 100644
index 0000000..5b878b0
--- /dev/null
+++ b/.devcontainer/Dockerfile
@@ -0,0 +1,22 @@
+# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.245.2/containers/python-3-miniconda/.devcontainer/base.Dockerfile
+
+FROM mcr.microsoft.com/vscode/devcontainers/miniconda:0-3
+
+# [Choice] Node.js version: none, lts/*, 16, 14, 12, 10
+ARG NODE_VERSION="none"
+RUN if [ "${NODE_VERSION}" != "none" ]; then su vscode -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"; fi
+
+# Copy environment.yml (if found) to a temp location so we update the environment. Also
+# copy "noop.txt" so the COPY instruction does not fail if no environment.yml exists.
+COPY environment.yml* .devcontainer/noop.txt /tmp/conda-tmp/
+RUN if [ -f "/tmp/conda-tmp/environment.yml" ]; then umask 0002 && /opt/conda/bin/conda env update -n base -f /tmp/conda-tmp/environment.yml; fi \
+    && rm -rf /tmp/conda-tmp
+
+# [Optional] Uncomment to install a different version of Python than the default
+# RUN conda install -y python=3.6 \
+#     && pip install --no-cache-dir pipx \
+#     && pipx reinstall-all
+
+# [Optional] Uncomment this section to install additional OS packages.
+# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+#     && apt-get -y install --no-install-recommends <your-package-list-here>
diff --git a/.devcontainer/add-notice.sh b/.devcontainer/add-notice.sh
new file mode 100644
index 0000000..c292bc5
--- /dev/null
+++ b/.devcontainer/add-notice.sh
@@ -0,0 +1,19 @@
+# Display a notice when not running in GitHub Codespaces
+
+cat << 'EOF' > /usr/local/etc/vscode-dev-containers/conda-notice.txt
+When using "conda" from outside of GitHub Codespaces, note the Anaconda repository
+contains restrictions on commercial use that may impact certain organizations. See
+https://aka.ms/vscode-remote/conda/miniconda
+
+EOF
+
+notice_script="$(cat << 'EOF'
+if [ -t 1 ] && [ "${IGNORE_NOTICE}" != "true" ] && [ "${TERM_PROGRAM}" = "vscode" ] && [ "${CODESPACES}" != "true" ] && [ ! -f "$HOME/.config/vscode-dev-containers/conda-notice-already-displayed" ]; then
+    cat "/usr/local/etc/vscode-dev-containers/conda-notice.txt"
+    mkdir -p "$HOME/.config/vscode-dev-containers"
+    ((sleep 10s; touch "$HOME/.config/vscode-dev-containers/conda-notice-already-displayed") &)
+fi
+EOF
+)"
+
+echo "${notice_script}" | tee -a /etc/bash.bashrc >> /etc/zsh/zshrc
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000..715c4c7
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,54 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
+// https://github.com/microsoft/vscode-dev-containers/tree/v0.245.2/containers/python-3-miniconda
+{
+	"name": "Miniconda (Python 3)",
+	"build": { 
+		"context": "..",
+		"dockerfile": "Dockerfile",
+		"args": {
+			"NODE_VERSION": "16"
+		}
+	},
+
+	// Configure tool-specific properties.
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			// Set *default* container specific settings.json values on container create.
+			"settings": { 
+				"python.defaultInterpreterPath": "/opt/conda/bin/python",
+				"python.linting.enabled": true,
+				"python.linting.pylintEnabled": true,
+				"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+				"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+				"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+				"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+				"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+				"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+				"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+				"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+				"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint"
+			},
+			
+			// Add the IDs of extensions you want installed when the container is created.
+			"extensions": [
+				"ms-python.python",
+				"ms-python.vscode-pylance"
+			]
+		}
+	},
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// Use 'postCreateCommand' to run commands after the container is created.
+	// "postCreateCommand": "python --version",
+
+	// Comment out to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
+	"remoteUser": "vscode",
+	"features": {
+		"docker-in-docker": "latest",
+		"github-cli": "latest",
+		"homebrew": "latest"
+	}
+}
diff --git a/.devcontainer/noop.txt b/.devcontainer/noop.txt
new file mode 100644
index 0000000..abee195
--- /dev/null
+++ b/.devcontainer/noop.txt
@@ -0,0 +1,3 @@
+This file is copied into the container along with environment.yml* from the
+parent folder. This is done to prevent the Dockerfile COPY instruction from 
+failing if no environment.yml is found.
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..22c2f34
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+notebooks/data/
+notebooks/hymenoptera_data/
+notebooks/tmp/
+notebooks/test.ipynb
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
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+
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+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
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+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
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+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
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+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
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+
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+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
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+      Work (including but not limited to damages for loss of goodwill,
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+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
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diff --git a/README.md b/README.md
index 8d086e0..55d9158 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,16 @@
-# 🐙 ML Notebooks
+# 🐙 Machine Learning Notebooks
 
-A series of code examples for all sorts of machine learning tasks and applications.
+This repo contains machine learning notebooks for different tasks and applications. The notebooks are meant to be minimal, easily reusable, and extendable. You are free to use them for educational and research purposes.
 
-The notebooks are meant to be minimal and easily reusable and extendable. 
+This repo supports Codespaces! 
+- Spin up a new instance by clicking on the green `"<> Code"` button followed by the `"Configure and create codespace"` option. Make sure to select the dev container config provided with this repo. This setups an environment with all the dependencies installed and ready to go.
+- Once the codespace is fully running, you can install all the libraries you will need to run the notebooks under the `/notebooks` folder. Open up a terminal and simply run `conda create --name myenv --file spec-file.txt` to install all the Python libraries including PyTorch.
+- Activate your environment `conda activate myenv`. *You might need to run `conda init zsh` or whatever shell you are using... and then close + reopen terminal.*
+- Finally you can try out if everything is working by opening a notebook such as `/notebooks/bow.ipynb`.
 
-Feel free to use them for educational and research purposes.
+---
+
+## Getting Started
 
 <table class="tg">
   <tr>
@@ -37,6 +43,22 @@ Feel free to use them for educational and research purposes.
 </a></td>
   </tr>
   
+  <tr>
+    <td class="tg-yw4l">Counterfactual Explanations</td>
+    <td class="tg-yw4l">A basic tutorial to learn about counterfactual explanations for explainable AI</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1mTSRjqki3VsH9MVPfNtJ5nJxcCHvL8B6?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a></td>
+  </tr>
+
+  <tr>
+    <td class="tg-yw4l">Linear Regression from Scratch</td>
+    <td class="tg-yw4l">An implementation of linear regression from scratch using stochastic gradient descent</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1br0hM79ORTVNXUpVgkV5t4o4AigGxfwk?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a></td>
+  </tr>
+  
   <tr>
     <td class="tg-yw4l">Logistic Regression from Scratch</td>
     <td class="tg-yw4l">An implementation of logistic regression from scratch</td>
@@ -69,14 +91,57 @@ Feel free to use them for educational and research purposes.
   <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
 </a></td>
   </tr>
-  
-  <tr>
+    <tr>
     <td class="tg-yw4l">Introduction to GNNs</td>
     <td class="tg-yw4l">Introduction to Graph Neural Networks. Applies basic GCN to Cora dataset for node classification.</td>
     <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1d0jLDwgNBtjBVQOFe8lO_1WrqTVeVZx9?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
 </a></td>
   </tr>
+ 
+  </table> 
+  
+  
+## NLP  
+<table class="tg">
+  <tr>
+    <th class="tg-yw4l"><b>Name</b></th>
+    <th class="tg-yw4l"><b>Description</b></th>
+    <th class="tg-yw4l"><b>Notebook</b></th>
+  </tr>
+  
+  <tr>
+    <td class="tg-yw4l">Bag of Words Text Classifier</td>
+    <td class="tg-yw4l">Build a simple bag of words text classifier.</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/19suDts9MNIhx0TeGO26_BIY2Xc0n6DBC?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a></td>
+  </tr>
+  
+  <tr>
+    <td class="tg-yw4l">Continuous Bag of Words (CBOW) Text Classifier</td>
+    <td class="tg-yw4l">Build a continuous bag of words text classifier.</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1lqS67-mbCspIKzx6y9wn7CuP96utWzP2?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a></td>
+  </tr>
+  
+  <tr>
+    <td class="tg-yw4l">Deep Continuous Bag of Words (Deep CBOW) Text Classifier</td>
+    <td class="tg-yw4l">Build a deep continuous bag of words text classifier.</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/18yz-qvMQYIYZt1BLihSJrKQZXh8zjH8x?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a></td>
+  </tr>
+ 
+  
+  <tr>
+    <td class="tg-yw4l">Text Data Augmentation</td>
+    <td class="tg-yw4l">An introduction to the most commonly used data augmentation techniques for text and their implementation</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1kyLaRevVf7VVy9BxJBJaL_ET4wyKSi-S?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a></td>
+  </tr>  
   
   <tr>
     <td class="tg-yw4l">Emotion Classification with Fine-tuned BERT</td>
@@ -86,20 +151,78 @@ Feel free to use them for educational and research purposes.
 </a></td>
   </tr>
   
+  </table> 
+
+## Transformers
+ <table class="tg"> 
+  
+  <tr>
+    <th class="tg-yw4l"><b>Name</b></th>
+    <th class="tg-yw4l"><b>Description</b></th>
+    <th class="tg-yw4l"><b>Notebook</b></th>
+  </tr>
+  
    <tr>
-    <td class="tg-yw4l">Text Classification  using Attention Mechanism and Positional Embeddings</td>
+    <td class="tg-yw4l">Text Classification using Transformer</td>
     <td class="tg-yw4l">An implementation of Attention Mechanism and Positional Embeddings on a text classification  task</td>
     <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1Jc-_kcO3xYHDMFYSsIcUimcc38_WFlyh?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
-</a></td>
+</a><br>
+      <a href="https://www.kaggle.com/code/ritvik1909/text-classification-attention" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
   </tr>
 
    <tr>
+    <td class="tg-yw4l">Neural Machine Translation using Transformer</td>
+    <td class="tg-yw4l">An implementation of Transformer to translate human readable dates in any format to YYYY-MM-DD format.</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1Y6JhWMmgU52MU9vyrOnHf6iK760Qx35h?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/neural-machine-translation-attention" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>   
+
+   <tr>
+    <td class="tg-yw4l">Feature Tokenizer Transformer</td>
+    <td class="tg-yw4l">An implementation of Feature Tokenizer Transformer on a classification task</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1tdPifaZCTVpjzCh1FOyGPywMEfrbBZLh?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/feature-tokenizer-transformer/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>     
+
+   <tr>
+    <td class="tg-yw4l">Named Entity Recognition using Transformer</td>
+    <td class="tg-yw4l">An implementation of Transformer to perform token classification and identify species in PubMed abstracts</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/12AdzQuOvFMrhlVI92dfaVpttkgBcgWcf?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/named-entity-recognition-attention" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>   
+  
+   <tr>
+    <td class="tg-yw4l">Extractive Question Answering using Transformer</td>
+    <td class="tg-yw4l">An implementation of Transformer to perform extractive question answering</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1Eq3PkrItGTZMTww-wlzume2j4nkQYsdP?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/question-answering-attention/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>    
+ </table> 
+ 
+## Computer Vision
+
+<table class="tg">
+  <tr>
+    <th class="tg-yw4l"><b>Name</b></th>
+    <th class="tg-yw4l"><b>Description</b></th>
+    <th class="tg-yw4l"><b>Notebook</b></th>
+  </tr>
+  <tr>
     <td class="tg-yw4l">Siamese Network</td>
     <td class="tg-yw4l">An implementation of Siamese Network for finding Image Similarity</td>
     <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1sn7BDKVvi8-Ng37gvfyNw8OCf8kZY91o?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
-</a></td>
+</a><br>
+     <a href="https://kaggle.com/code/ritvik1909/siamese-network" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
   </tr>  
 
    <tr>
@@ -107,11 +230,114 @@ Feel free to use them for educational and research purposes.
     <td class="tg-yw4l">An implementation of Variational Auto Encoder to generate Augmentations for MNIST Handwritten Digits</td>
     <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/13na-qaRSMwD2jplXjg_oapPDmVozIzLL?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
-</a></td>
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/variational-auto-encoder" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
   </tr>    
 
+   <tr>
+    <td class="tg-yw4l">Object Detection using Sliding Window and Image Pyramid</td>
+    <td class="tg-yw4l">A basic object detection implementation using sliding window and image pyramid on top of an image classifier</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1CiLN6g7puBheHq-tfk4YB4X-Lgypur8M?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/object-detection-sliding-window" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>    
   
- </table> 
+   <tr>
+    <td class="tg-yw4l">Object Detection using Selective Search</td>
+    <td class="tg-yw4l">A basic object detection implementation using selective search on top of an image classifier</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1C7Zo5JRMrcETMxn-8jRuszrEXXjRbN37?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/object-detection-selective-search" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>  
+  
+</table> 
+
+## Generative Adversarial Network
+<table class="tg">
+  <tr>
+    <th class="tg-yw4l"><b>Name</b></th>
+    <th class="tg-yw4l"><b>Description</b></th>
+    <th class="tg-yw4l"><b>Notebook</b></th>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">Deep Convolutional GAN</td>
+    <td class="tg-yw4l">An Implementation of Deep Convolutional GAN to generate MNIST digits</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1ss9FCHvI0ZAuS1PJL6RxlgEhM_glZR6S?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/deep-convolutional-gan/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>  
+  <tr>
+    <td class="tg-yw4l">Wasserstein GAN with Gradient Penalty</td>
+    <td class="tg-yw4l">An Implementation of Wasserstein GAN with Gradient Penalty to generate MNIST digits</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1x7aDlxkv3PYev2AsmZRl8pII2zsY_DVs?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/wasserstein-gan-with-gradient-penalty/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>  
+  <tr>
+    <td class="tg-yw4l">Conditional GAN</td>
+    <td class="tg-yw4l">An Implementation of Conditional GAN to generate MNIST digits</td>
+    <td class="tg-yw4l"><a href="https://colab.research.google.com/drive/1Z74CpEu76e6A62gxKXIJ8u8xyVjF21_k?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
+</a><br>
+     <a href="https://www.kaggle.com/code/ritvik1909/conditional-gan/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>  
+  
+</table>
+ 
+  ## Parameter Efficient Fine-tuning
+
+<table class="tg">
+  <tr>
+    <th class="tg-yw4l"><b>Name</b></th>
+    <th class="tg-yw4l"><b>Description</b></th>
+    <th class="tg-yw4l"><b>Notebook</b></th>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">LoRA BERT</td>
+    <td class="tg-yw4l">An Implementation of BERT Finetuning using LoRA</td>
+    <td class="tg-yw4l">
+     <a href="https://www.kaggle.com/code/ritvik1909/lora-bert/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">LoRA BERT NER</td>
+    <td class="tg-yw4l">An Implementation of BERT Finetuning using LoRA for token classification task</td>
+    <td class="tg-yw4l">
+     <a href="https://www.kaggle.com/code/ritvik1909/lora-bert-ner/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">LoRA T5</td>
+    <td class="tg-yw4l">An Implementation of T5 Finetuning using LoRA</td>
+    <td class="tg-yw4l">
+     <a href="https://www.kaggle.com/code/ritvik1909/lora-t5/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">LoRA TinyLlama 1.1B</td>
+    <td class="tg-yw4l">An Implementation of TinyLlama 1.1B Finetuning using LoRA</td>
+    <td class="tg-yw4l">
+     <a href="https://www.kaggle.com/code/ritvik1909/lora-tinyllama-1-1b/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">QLoRA TinyLlama 1.1B</td>
+    <td class="tg-yw4l">An Implementation of TinyLlama 1.1B Finetuning using QLoRA</td>
+    <td class="tg-yw4l">
+     <a href="https://www.kaggle.com/code/ritvik1909/qlora-tinyllama-1-1b/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>
+  <tr>
+    <td class="tg-yw4l">QLoRA Mistral 7B</td>
+    <td class="tg-yw4l">An Implementation of Mistral 7B Finetuning using QLoRA</td>
+    <td class="tg-yw4l">
+     <a href="https://www.kaggle.com/code/ritvik1909/qlora-mistral-7b/" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a></td>
+  </tr>
+
+</table>
+
+---
+
+If you find any bugs or have any questions regarding these notebooks, please open an issue. We will address it as soon as we can. 
 
 Reach out on [Twitter](https://twitter.com/omarsar0) if you have any questions. 
 
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diff --git a/notebooks/bow-dataloader.ipynb b/notebooks/bow-dataloader.ipynb
new file mode 100644
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--- /dev/null
+++ b/notebooks/bow-dataloader.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bag of Words Text Classifier\n",
+    "\n",
+    "The code below implements a simple bag of words text classifier.\n",
+    "- We tokenize the text, create a vocabulary and encode each piece of text in the dataset\n",
+    "- The lookup allows for extracting embeddings for each tokenized inputs\n",
+    "- The embedding vectors are added together with a bias vector\n",
+    "- The resulting vector is referred to as the scores\n",
+    "- The score are applied a softmax to generate probabilities which are used for the classification task\n",
+    "\n",
+    "The code used in this notebook was inspired by code from the [official repo](https://github.com/neubig/nn4nlp-code) used in the [CMU Neural Networks for NLP class](http://www.phontron.com/class/nn4nlp2021/schedule.html) by [Graham Neubig](http://www.phontron.com/index.php). \n",
+    "\n",
+    "We are also adding a PyTorch data loader to this notebook which is how it differs from `bow.ipynb`.\n",
+    "\n",
+    "![img txt](../img/bow.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "# download the files\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/dev.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/test.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/train.txt\n",
+    "\n",
+    "# create the data folders\n",
+    "!mkdir data data/classes\n",
+    "!cp dev.txt data/classes\n",
+    "!cp test.txt data/classes\n",
+    "!cp train.txt data/classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, process each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, 'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.lower().strip()\n",
+    "            line = line.split(' ||| ')\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "train_data = read_data('data/classes/train.txt')\n",
+    "test_data = read_data('data/classes/test.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Construct the Vocab and Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating the word and tag indices\n",
+    "word_to_index = {}\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # adds <UNK> to dictionary\n",
+    "tag_to_index = {}\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line[1].split(\" \"):\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "            else:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "\n",
+    "        if line[0] not in tag_to_index:\n",
+    "            tag_to_index[line[0]] = len(tag_to_index)\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(test_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield [[word_to_index[word] for word in line[1].split(\" \")], tag_to_index[line[0]]]\n",
+    "\n",
+    "train_data = [*create_tensor(train_data)]\n",
+    "test_data = [*create_tensor(test_data)]\n",
+    "\n",
+    "number_of_words = len(word_to_index)\n",
+    "number_of_tags = len(tag_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert data to PyTorch Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data import Dataset\n",
+    "\n",
+    "# load data into a dataset and dataloader; ensure that the data is split X, y\n",
+    "class TextDataset(Dataset):\n",
+    "    def __init__(self, data):\n",
+    "        self.data = data\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.data)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        return torch.as_tensor(self.data[idx][0]), torch.as_tensor(self.data[idx][1])\n",
+    "\n",
+    "train_dataset = TextDataset(train_data)\n",
+    "test_dataset = TextDataset(test_data)\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=1, shuffle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cpu or gpu\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "# create a simple neural network with embedding layer, bias, and xavier initialization\n",
+    "class BoW(torch.nn.Module):\n",
+    "    def __init__(self, nwords, ntags):\n",
+    "        super(BoW, self).__init__()\n",
+    "        self.embedding = nn.Embedding(nwords, ntags)\n",
+    "        nn.init.xavier_uniform_(self.embedding.weight)\n",
+    "\n",
+    "        type = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor\n",
+    "        self.bias = torch.zeros(ntags, requires_grad=True).type(type)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        emb = self.embedding(x) # seq_len x ntags (for each seq) \n",
+    "        out = torch.sum(emb, dim=0) + self.bias # ntags\n",
+    "        out = out.view(1, -1) # reshape to (1, ntags)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ITER: 1 | train loss/sent: 1.4731 | train accuracy: 0.3668 | test accuracy: 0.3778\n",
+      "ITER: 2 | train loss/sent: 1.1223 | train accuracy: 0.6056 | test accuracy: 0.4118\n",
+      "ITER: 3 | train loss/sent: 0.9106 | train accuracy: 0.7155 | test accuracy: 0.4186\n",
+      "ITER: 4 | train loss/sent: 0.7685 | train accuracy: 0.7687 | test accuracy: 0.4032\n",
+      "ITER: 5 | train loss/sent: 0.6635 | train accuracy: 0.8070 | test accuracy: 0.4054\n",
+      "ITER: 6 | train loss/sent: 0.5814 | train accuracy: 0.8346 | test accuracy: 0.4113\n",
+      "ITER: 7 | train loss/sent: 0.5157 | train accuracy: 0.8558 | test accuracy: 0.3991\n",
+      "ITER: 8 | train loss/sent: 0.4631 | train accuracy: 0.8722 | test accuracy: 0.3946\n",
+      "ITER: 9 | train loss/sent: 0.4183 | train accuracy: 0.8839 | test accuracy: 0.4014\n",
+      "ITER: 10 | train loss/sent: 0.3807 | train accuracy: 0.8969 | test accuracy: 0.3928\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train and test the BoW model\n",
+    "model = BoW(number_of_words, number_of_tags).to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters())\n",
+    "type = torch.LongTensor\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "    type = torch.cuda.LongTensor\n",
+    "\n",
+    "# perform training of the Bow model\n",
+    "def train_bow(model, optimizer, criterion, train_data):\n",
+    "    for ITER in range(10):\n",
+    "        # perform training\n",
+    "        model.train()\n",
+    "        total_loss = 0.0\n",
+    "        train_correct = 0\n",
+    "        for batch, (sentence, tag) in enumerate(train_loader):\n",
+    "            sentence = sentence[0].to(device)\n",
+    "            tag = tag.to(device)\n",
+    "\n",
+    "            output = model(sentence)\n",
+    "            predicted = torch.argmax(output.data.detach()).item()\n",
+    "            \n",
+    "            loss = criterion(output, tag)\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            if predicted == tag: train_correct+=1\n",
+    "\n",
+    "        # perform testing of the model\n",
+    "        model.eval()\n",
+    "        test_correct = 0\n",
+    "        for batch, (sentence, tag) in enumerate(test_loader):\n",
+    "            sentence = sentence[0].to(device)\n",
+    "            output = model(sentence)\n",
+    "            predicted = torch.argmax(output.data.detach()).item()\n",
+    "            if predicted == tag: test_correct += 1\n",
+    "        \n",
+    "        # print model performance results\n",
+    "        log = f'ITER: {ITER+1} | ' \\\n",
+    "            f'train loss/sent: {total_loss/len(train_data):.4f} | ' \\\n",
+    "            f'train accuracy: {train_correct/len(train_data):.4f} | ' \\\n",
+    "            f'test accuracy: {test_correct/len(test_data):.4f}'\n",
+    "        print(log)\n",
+    "\n",
+    "# call the train_bow function\n",
+    "train_bow(model, optimizer, criterion, train_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises\n",
+    "\n",
+    "To keep on practising, you can try the following exercises:\n",
+    "\n",
+    "- Try to use different batch sizes and see how it affects the training.\n",
+    "- Try to use [`torchtext`](https://pytorch.org/text/stable/index.html#) to load other datasets and create tokenizer and vocabularies for them. This [example](https://pytorch.org/tutorials/beginner/transformer_tutorial.html) on Transformer could be useful to help guide you.\n",
+    "- Write a mini Python library easily help you train and evaluate models. You can use the code from this notebook as a starting point."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/bow.ipynb b/notebooks/bow.ipynb
new file mode 100644
index 0000000..5a85f63
--- /dev/null
+++ b/notebooks/bow.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bag of Words Text Classifier\n",
+    "\n",
+    "The code below implements a simple bag of words text classifier.\n",
+    "- We tokenize the text, create a vocabulary and encode each piece of text in the dataset\n",
+    "- The lookup allows for extracting embeddings for each tokenized inputs\n",
+    "- The embedding vectors are added together with a bias vector\n",
+    "- The resulting vector is referred to as the scores\n",
+    "- The score are applied a softmax to generate probabilities which are used for the classification task\n",
+    "\n",
+    "The code used in this notebook was inspired by code from the [official repo](https://github.com/neubig/nn4nlp-code) used in the [CMU Neural Networks for NLP class](http://www.phontron.com/class/nn4nlp2021/schedule.html) by [Graham Neubig](http://www.phontron.com/index.php). \n",
+    "\n",
+    "![img txt](../img/bow.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "# download the files\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/dev.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/test.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/train.txt\n",
+    "\n",
+    "# create the data folders\n",
+    "!mkdir data data/classes\n",
+    "!cp dev.txt data/classes\n",
+    "!cp test.txt data/classes\n",
+    "!cp train.txt data/classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, process each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, 'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.lower().strip()\n",
+    "            line = line.split(' ||| ')\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "train_data = read_data('data/classes/train.txt')\n",
+    "test_data = read_data('data/classes/test.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Construct the Vocab and Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating the word and tag indices\n",
+    "word_to_index = {}\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # adds <UNK> to dictionary\n",
+    "tag_to_index = {}\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line[1].split(\" \"):\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "            else:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "\n",
+    "        if line[0] not in tag_to_index:\n",
+    "            tag_to_index[line[0]] = len(tag_to_index)\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(test_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield([word_to_index[word] for word in line[1].split(\" \")], tag_to_index[line[0]])\n",
+    "\n",
+    "train_data = list(create_tensor(train_data))\n",
+    "test_data = list(create_tensor(test_data))\n",
+    "\n",
+    "number_of_words = len(word_to_index)\n",
+    "number_of_tags = len(tag_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cpu or gpu\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "# create a simple neural network with embedding layer, bias, and xavier initialization\n",
+    "class BoW(torch.nn.Module):\n",
+    "    def __init__(self, nwords, ntags):\n",
+    "        super(BoW, self).__init__()\n",
+    "        self.embedding = nn.Embedding(nwords, ntags)\n",
+    "        nn.init.xavier_uniform_(self.embedding.weight)\n",
+    "\n",
+    "        type = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor\n",
+    "        self.bias = torch.zeros(ntags, requires_grad=True).type(type)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        emb = self.embedding(x) # seq_len x ntags (for each seq) \n",
+    "        out = torch.sum(emb, dim=0) + self.bias # ntags\n",
+    "        out = out.view(1, -1) # reshape to (1, ntags)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretest the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 0.0124,  0.0164, -0.0182, -0.0014, -0.0120]], device='cuda:0',\n",
+       "       grad_fn=<ViewBackward0>)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# function to convert sentence into tensor using word_to_index dictionary\n",
+    "def sentence_to_tensor(sentence):\n",
+    "    return torch.LongTensor([word_to_index[word] for word in sentence.split(\" \")])\n",
+    "\n",
+    "# test the sentence_to_tensor function\n",
+    "type = torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor\n",
+    "out = sentence_to_tensor(\"i love dogs\").type(type)\n",
+    "test_model = BoW(number_of_words, number_of_tags).to(device)\n",
+    "test_model(out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ITER: 1 | train loss/sent: 1.4733 | train accuracy: 0.3631 | test accuracy: 0.4009\n",
+      "ITER: 2 | train loss/sent: 1.1216 | train accuracy: 0.6040 | test accuracy: 0.4118\n",
+      "ITER: 3 | train loss/sent: 0.9123 | train accuracy: 0.7117 | test accuracy: 0.4154\n",
+      "ITER: 4 | train loss/sent: 0.7688 | train accuracy: 0.7664 | test accuracy: 0.4140\n",
+      "ITER: 5 | train loss/sent: 0.6631 | train accuracy: 0.8065 | test accuracy: 0.4068\n",
+      "ITER: 6 | train loss/sent: 0.5814 | train accuracy: 0.8324 | test accuracy: 0.4059\n",
+      "ITER: 7 | train loss/sent: 0.5171 | train accuracy: 0.8507 | test accuracy: 0.4077\n",
+      "ITER: 8 | train loss/sent: 0.4640 | train accuracy: 0.8695 | test accuracy: 0.4036\n",
+      "ITER: 9 | train loss/sent: 0.4191 | train accuracy: 0.8830 | test accuracy: 0.3991\n",
+      "ITER: 10 | train loss/sent: 0.3818 | train accuracy: 0.8929 | test accuracy: 0.3964\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train and test the BoW model\n",
+    "model = BoW(number_of_words, number_of_tags).to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters())\n",
+    "type = torch.LongTensor\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "    type = torch.cuda.LongTensor\n",
+    "\n",
+    "# perform training of the Bow model\n",
+    "def train_bow(model, optimizer, criterion, train_data):\n",
+    "    for ITER in range(10):\n",
+    "        # perform training\n",
+    "        model.train()\n",
+    "        random.shuffle(train_data)\n",
+    "        total_loss = 0.0\n",
+    "        train_correct = 0\n",
+    "        for sentence, tag in train_data:\n",
+    "            sentence = torch.tensor(sentence).type(type)\n",
+    "            tag = torch.tensor([tag]).type(type)\n",
+    "            output = model(sentence)\n",
+    "            predicted = torch.argmax(output.data.detach()).item()\n",
+    "            \n",
+    "            loss = criterion(output, tag)\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            if predicted == tag: train_correct+=1\n",
+    "\n",
+    "        # perform testing of the model\n",
+    "        model.eval()\n",
+    "        test_correct = 0\n",
+    "        for sentence, tag in test_data:\n",
+    "            sentence = torch.tensor(sentence).type(type)\n",
+    "            output = model(sentence)\n",
+    "            predicted = torch.argmax(output.data.detach()).item()\n",
+    "            if predicted == tag: test_correct += 1\n",
+    "        \n",
+    "        # print model performance results\n",
+    "        log = f'ITER: {ITER+1} | ' \\\n",
+    "            f'train loss/sent: {total_loss/len(train_data):.4f} | ' \\\n",
+    "            f'train accuracy: {train_correct/len(train_data):.4f} | ' \\\n",
+    "            f'test accuracy: {test_correct/len(test_data):.4f}'\n",
+    "        print(log)\n",
+    "\n",
+    "# call the train_bow function\n",
+    "train_bow(model, optimizer, criterion, train_data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cbow.ipynb b/notebooks/cbow.ipynb
new file mode 100644
index 0000000..85d382f
--- /dev/null
+++ b/notebooks/cbow.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Continuous Bag of Words (CBOW) Text Classifier\n",
+    "\n",
+    "The code below implements a continuous bag of words text classifier.\n",
+    "- We tokenize the text, create a vocabulary and encode each piece of text in the dataset\n",
+    "- The lookup allows for extracting embeddings for each tokenized input\n",
+    "- The embedding vectors are added together\n",
+    "- The resulting vector is multiplied with a weight matrix, which is then added a bias vector; this results in scores\n",
+    "- The scores are applied a softmax to generate probabilities which are used for the final classification\n",
+    "\n",
+    "The code used in this notebook was inspired by code from the [official repo](https://github.com/neubig/nn4nlp-code) used in the [CMU Neural Networks for NLP class](http://www.phontron.com/class/nn4nlp2021/schedule.html) by [Graham Neubig](http://www.phontron.com/index.php). \n",
+    "\n",
+    "![img txt](../img/cbow.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "% % capture\n",
+    "''' uncomment to download the data\n",
+    "# download the files\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/dev.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/test.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/train.txt\n",
+    "\n",
+    "# create the data folders\n",
+    "!mkdir data data/classes\n",
+    "!cp dev.txt data/classes\n",
+    "!cp test.txt data/classes\n",
+    "!cp train.txt data/classes\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read and Process Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, process each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, 'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.lower().strip()\n",
+    "            line = line.split(' ||| ')\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "train_data = read_data('data/classes/train.txt')\n",
+    "test_data = read_data('data/classes/test.txt')\n",
+    "\n",
+    "# creating the word and tag indices\n",
+    "word_to_index = {}\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index)  # add <UNK> to dictionary\n",
+    "tag_to_index = {}\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line[1].split(\" \"):\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "            else:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "\n",
+    "        if line[0] not in tag_to_index:\n",
+    "            tag_to_index[line[0]] = len(tag_to_index)\n",
+    "\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(test_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield ([word_to_index[word] for word in line[1].split(\" \")], tag_to_index[line[0]])\n",
+    "\n",
+    "\n",
+    "train_data = list(create_tensor(train_data))\n",
+    "test_data = list(create_tensor(test_data))\n",
+    "\n",
+    "number_of_words = len(word_to_index)\n",
+    "number_of_tags = len(tag_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'torch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_3236/4002993260.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# cpu or gpu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# create a simple neural network with embedding layer, bias, and xavier initialization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mCBoW\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# cpu or gpu\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "# create a simple neural network with embedding layer, bias, and xavier initialization\n",
+    "class CBoW(torch.nn.Module):\n",
+    "    def __init__(self, nwords, ntags, emb_size):\n",
+    "        super(CBoW, self).__init__()\n",
+    "\n",
+    "        # layers\n",
+    "        self.embedding = torch.nn.Embedding(nwords, emb_size)\n",
+    "        self.linear = torch.nn.Linear(emb_size, ntags)\n",
+    "\n",
+    "        # use xavier initialization for weights\n",
+    "        nn.init.xavier_uniform_(self.embedding.weight)\n",
+    "        nn.init.xavier_uniform_(self.linear.weight)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        emb = self.embedding(x)  # seq x emb_size\n",
+    "        out = torch.sum(emb, dim=0)  # emb_size\n",
+    "        out = out.view(1, -1)  # reshape to (1, emb_size)\n",
+    "        out = self.linear(out)  # 1 x ntags\n",
+    "        return out\n",
+    "\n",
+    "\n",
+    "EMB_SIZE = 64\n",
+    "model = CBoW(number_of_words, number_of_tags, EMB_SIZE)\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters())\n",
+    "type = torch.LongTensor\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "    type = torch.cuda.LongTensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 | train loss/sent: 1.4111 | train accuracy: 0.3841 | test accuracy: 0.3982\n",
+      "epoch: 2 | train loss/sent: 0.8886 | train accuracy: 0.6522 | test accuracy: 0.3991\n",
+      "epoch: 3 | train loss/sent: 0.5147 | train accuracy: 0.8148 | test accuracy: 0.3950\n",
+      "epoch: 4 | train loss/sent: 0.3200 | train accuracy: 0.8878 | test accuracy: 0.3796\n",
+      "epoch: 5 | train loss/sent: 0.2148 | train accuracy: 0.9247 | test accuracy: 0.3738\n",
+      "epoch: 6 | train loss/sent: 0.1536 | train accuracy: 0.9466 | test accuracy: 0.3783\n",
+      "epoch: 7 | train loss/sent: 0.1097 | train accuracy: 0.9618 | test accuracy: 0.3638\n",
+      "epoch: 8 | train loss/sent: 0.0797 | train accuracy: 0.9716 | test accuracy: 0.3692\n",
+      "epoch: 9 | train loss/sent: 0.0568 | train accuracy: 0.9805 | test accuracy: 0.3661\n",
+      "epoch: 10 | train loss/sent: 0.0435 | train accuracy: 0.9853 | test accuracy: 0.3529\n"
+     ]
+    }
+   ],
+   "source": [
+    "# perform training of the Bow model\n",
+    "\n",
+    "for epoch in range(10):\n",
+    "    # perform training\n",
+    "    model.train()\n",
+    "    random.shuffle(train_data)\n",
+    "    total_loss = 0.0\n",
+    "    train_correct = 0\n",
+    "    for sentence, tag in train_data:\n",
+    "        sentence = torch.tensor(sentence).type(type)\n",
+    "        tag = torch.tensor([tag]).type(type)\n",
+    "        output = model(sentence)\n",
+    "        predicted = torch.argmax(output.data.detach()).item()\n",
+    "\n",
+    "        loss = criterion(output, tag)\n",
+    "        total_loss += loss.item()\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if predicted == tag:\n",
+    "            train_correct += 1\n",
+    "\n",
+    "    # perform testing of the model\n",
+    "    model.eval()\n",
+    "    test_correct = 0\n",
+    "    for sentence, tag in test_data:\n",
+    "        sentence = torch.tensor(sentence).type(type)\n",
+    "        output = model(sentence)\n",
+    "        predicted = torch.argmax(output.data.detach()).item()\n",
+    "        if predicted == tag:\n",
+    "            test_correct += 1\n",
+    "\n",
+    "    # print model performance results\n",
+    "    log = f'epoch: {epoch+1} | ' \\\n",
+    "        f'train loss/sent: {total_loss/len(train_data):.4f} | ' \\\n",
+    "        f'train accuracy: {train_correct/len(train_data):.4f} | ' \\\n",
+    "        f'test accuracy: {test_correct/len(test_data):.4f}'\n",
+    "    print(log)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/comp_graphs.ipynb b/notebooks/comp_graphs.ipynb
new file mode 100644
index 0000000..f82c1df
--- /dev/null
+++ b/notebooks/comp_graphs.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to Computational Graphs with PyTorch\n",
+    "\n",
+    "by [Elvis Saravia](https://twitter.com/omarsar0)\n",
+    "\n",
+    "\n",
+    "In this notebook we provide a short introduction and overview of computational graphs using PyTorch.\n",
+    "\n",
+    "There are several materials online that cover theory on the topic of computational graphs. However, I think it's much easier to learn the concept using code. I attempt to bridge the gap here which should be useful for beginner students.  \n",
+    "\n",
+    "Inspired by Olah's article [\"Calculus on Computational Graphs: Backpropagation\"](https://colah.github.io/posts/2015-08-Backprop/), I've put together a few code snippets to get you started with computationsl graphs with PyTorch. This notebook should complement that article, so refer to it for more comprehensive explanations. In fact, I've tried to simplify the explanations and refer to them here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Why Computational Graphs?\n",
+    "\n",
+    "When talking about neural networks in any context, [backpropagation](https://en.wikipedia.org/wiki/Backpropagation) is an important topic to understand because it is the algorithm used for training deep neural networks. \n",
+    "\n",
+    "Backpropagation is used to calculate derivatives which is what you need to keep optimizing parameters of the model and allowing the model to learn on the task at hand. \n",
+    "\n",
+    "Many of the deep learning frameworks today like PyTorch does the backpropagation out-of-the-box using [**automatic differentiation**](https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html). \n",
+    "\n",
+    "To better understand how this is done it's important to talk about **computational graphs** which defines the flow of computations that are carried out throughout the network. Along the way we will use `torch.autograd` to demonstrate in code how this works. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting Started\n",
+    "\n",
+    "Inspired by Olah's article on computational graphs, let's look at the following expression $e = (a + b) * (b+1)$. It helps to break it down to the following operations:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}&c=a+b \\\\&d=b+1 \\\\&e=c * d\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This is not a neural network of any sort. We are just going through a very simple example of a chain of operations which you can be represented with computational graphs. \n",
+    "\n",
+    "Let's visualize these operations using a computational graph. Computational graphs contain **nodes** which can represent and input (tensor, matrix, vector, scalar) or **operation** that can be the input to another node. The nodes are connected by **edges**, which represent a function argument, they are pointers to nodes. Note that the computation graphs are directed and acyclic. The computational graph for our example looks as follows:\n",
+    "\n",
+    "![](https://colah.github.io/posts/2015-08-Backprop/img/tree-def.png)\n",
+    "\n",
+    "*Source: Christopher Olah (2015)*\n",
+    "\n",
+    "We can evaluate the expression by setting our input variables as follows: $a=2$ and $b=1$. This will allow us to compute nodes up through the graph as shown in the computational graph above.  \n",
+    "\n",
+    "Rather than doing this by hand, we can use the automatic differentation engine provided by PyTorch. \n",
+    "\n",
+    "Let's import PyTorch first:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the inputs like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = torch.tensor([2.], requires_grad=True)\n",
+    "b = torch.tensor([1.], requires_grad=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we used `requires_grad=True` to tell the autograd engine that every operation on them should be tracked. \n",
+    "\n",
+    "These are the operations in code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = a + b\n",
+    "d = b + 1\n",
+    "e = c * d\n",
+    "\n",
+    "# grads populated for non-leaf nodes\n",
+    "c.retain_grad()\n",
+    "d.retain_grad()\n",
+    "e.retain_grad()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we used `.retain_grad()` to allow gradients to be stored for non-leaf nodes as we are interested in inpecting those as well.\n",
+    "\n",
+    "Now that we have our computational graph, we can check the result when evaluating the expression:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([6.], grad_fn=<MulBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output is a tensor with the value of `6.`, which verifies the results here: \n",
+    "\n",
+    "![](https://colah.github.io/posts/2015-08-Backprop/img/tree-eval.png)\n",
+    "*Source: Christopher Olah (2015)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Derivatives on Computational Graphs\n",
+    "\n",
+    "Using the concept of computational graphs we are now interested in evaluating the **partial derivatives** of the edges of the graph. This will help in gathering the gradients of the graph. Remember that gradients are what we use to train the neural network and those calculations can be taken care of by the automatic differentation engine. \n",
+    "\n",
+    "The intuition is: we want to know, for example, if $a$ directly affects $c$, how does it affect it. In other words, if we change $a$ a little, how does $c$ change. This is referred to as the partial derivative of $c$ with respect to $a$.\n",
+    "\n",
+    "You can work this by hand, but the easy way to do this with PyTorch is by calling `.backward()` on $e$ and let the engine figure out the values. The `.backward()` signals the autograd engine to calculate the gradients and store them in the respective tensors’ `.grad` attribute.\n",
+    "\n",
+    "Let's do that now:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e.backward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let’s say we are interested in the derivative of $e$ with respect to $a$, how do we obtain this? In other words, we are looking for $\\frac{\\partial e}{\\partial a}$.\n",
+    "\n",
+    "Using PyTorch, we can do this by calling `a.grad`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([2.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a.grad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to understand the intuition behind this. Olah puts it best:\n",
+    "\n",
+    ">Let’s consider how $e$ is affected by $a$. If we change $a$ at a speed of 1, $c$ also changes at a speed of $1$. In turn, $c$ changing at a speed of $1$ causes $e$ to change at a speed of $2$. So $e$ changes at a rate of $1*2$ with respect to $a$.\n",
+    "\n",
+    "In other words, by hand this would be:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial e}{\\partial \\boldsymbol{a}}=\\frac{\\partial e}{\\partial \\boldsymbol{c}} \\frac{\\partial \\boldsymbol{c}}{\\partial \\boldsymbol{a}} = 2 * 1\n",
+    "$$\n",
+    "\n",
+    "You can verify that this is correct by checking the manual calculations by Olah. Since $a$ is not directly connectected to $e$, we can use some special rule which allows to sum over all paths from one node to the other in the computational graph and mulitplying the derivatives on each edge of the path together.\n",
+    "\n",
+    "![](https://colah.github.io/posts/2015-08-Backprop/img/tree-eval-derivs.png)\n",
+    "*Source: Christopher Olah (2015)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To check that this holds, let look at another example. How about caluclating the derivative of $e$ with respect to $b$, i.e., $\\frac{\\partial e}{\\partial b}$?\n",
+    "\n",
+    "We can get that through `b.grad`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([5.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(b.grad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you work it out by hand, you are basically doing the following:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial e}{\\partial b}=1 * 2+1 * 3\n",
+    "$$\n",
+    "\n",
+    "It indicates how $b$ affects $e$ through $c$ and $d$. We are essentially summing over paths in the computational graph.\n",
+    "\n",
+    "Here are all the gradients collected, including non-leaf nodes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([2.]) tensor([5.]) tensor([2.]) tensor([3.]) tensor([1.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a.grad, b.grad, c.grad, d.grad, e.grad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use the computational graph above to verify that everything is correct. This is the power of computational graphs and how they are used by automatic differentation engines. It's also a very useful concept to understand when developing neural networks architectures and their correctness.\n",
+    "\n",
+    "### Next Steps\n",
+    "\n",
+    "In this notebook, I've provided a simple and intuitive explanation to the concept of computational graphs using PyTorch. I highly recommend to go through [Olah's article](https://colah.github.io/posts/2015-08-Backprop/) for more on the topic.\n",
+    "\n",
+    "In the next tutorial, I will be applying the concept of computational graphs to more advanced operations you typically see in a neural network. In fact, if you are interested in this, and you are feeling comfortable with the topic now, you can check out these two PyTorch tutorials:\n",
+    "\n",
+    "- [A gentle introduction to `torch.autograd`](https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html)\n",
+    "- [Automatic differentation with `torch.autograd`](https://pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html)\n",
+    "\n",
+    "And here are some other useful references used to put this article together:\n",
+    "\n",
+    "- [Hacker's guide to Neural Networks\n",
+    "](http://karpathy.github.io/neuralnets/)\n",
+    "- [Backpropagation calculus](https://www.youtube.com/watch?v=tIeHLnjs5U8&ab_channel=3Blue1Brown)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('nlp')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/concise_log_reg.ipynb b/notebooks/concise_log_reg.ipynb
new file mode 100644
index 0000000..fa89bcf
--- /dev/null
+++ b/notebooks/concise_log_reg.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Concise Logistic Regression for Image Classification\n",
+    "\n",
+    "- Shows a concise implementation of logistic regression for image classification\n",
+    "- Uses PyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import torch\n",
+    "import torchvision\n",
+    "import torch.nn as nn\n",
+    "from torchvision import datasets, models, transforms\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# use gpu if available\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# download the data (uncomment if to download the data locally)\n",
+    "#!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+    "#!unzip hymenoptera_data.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create data loaders\n",
+    "\n",
+    "data_dir = 'hymenoptera_data'\n",
+    "\n",
+    "# custom transformer to flatten the image tensors\n",
+    "class ReshapeTransform:\n",
+    "    def __init__(self, new_size):\n",
+    "        self.new_size = new_size\n",
+    "\n",
+    "    def __call__(self, img):\n",
+    "        result = torch.reshape(img, self.new_size)\n",
+    "        return result\n",
+    "\n",
+    "# transformations used to standardize and normalize the datasets\n",
+    "data_transforms = {\n",
+    "    'train': transforms.Compose([\n",
+    "        transforms.Resize(224),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        ReshapeTransform((-1,)) # flattens the data\n",
+    "    ]),\n",
+    "    'val': transforms.Compose([\n",
+    "        transforms.Resize(224),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        ReshapeTransform((-1,)) # flattens the data\n",
+    "    ]),\n",
+    "}\n",
+    "\n",
+    "# load the correspoding folders\n",
+    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
+    "                                          data_transforms[x])\n",
+    "                  for x in ['train', 'val']}\n",
+    "\n",
+    "# load the entire dataset; we are not using minibatches here\n",
+    "train_dataset = torch.utils.data.DataLoader(image_datasets['train'],\n",
+    "                                            batch_size=len(image_datasets['train']),\n",
+    "                                            shuffle=True)\n",
+    "\n",
+    "test_dataset = torch.utils.data.DataLoader(image_datasets['val'],\n",
+    "                                           batch_size=len(image_datasets['val']),\n",
+    "                                           shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the LR model\n",
+    "class LR(nn.Module):\n",
+    "    def __init__(self, dim):\n",
+    "        super(LR, self).__init__()\n",
+    "        self.linear = nn.Linear(dim, 1)\n",
+    "        nn.init.zeros_(self.linear.weight)\n",
+    "        nn.init.zeros_(self.linear.bias)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.linear(x)\n",
+    "        x = torch.sigmoid(x)\n",
+    "        return x "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict function\n",
+    "def predict(yhat, y):\n",
+    "    yhat = yhat.squeeze()\n",
+    "    y = y.unsqueeze(0) \n",
+    "    y_prediction = torch.zeros(y.size()[1])\n",
+    "    for i in range(yhat.shape[0]):\n",
+    "        if yhat[i] <= 0.5:\n",
+    "            y_prediction[i] = 0\n",
+    "        else:\n",
+    "            y_prediction[i] = 1\n",
+    "    return 100 - torch.mean(torch.abs(y_prediction - y)) * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model config\n",
+    "dim = train_dataset.dataset[0][0].shape[0]\n",
+    "\n",
+    "lrmodel = LR(dim).to(device)\n",
+    "criterion = nn.BCELoss()\n",
+    "optimizer = torch.optim.SGD(lrmodel.parameters(), lr=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost after iteration 0: 0.6931471228599548 | Train Acc: 50.40983581542969 | Test Acc: 45.75163269042969\n",
+      "Cost after iteration 10: 0.6691471338272095 | Train Acc: 64.3442611694336 | Test Acc: 54.24836730957031\n",
+      "Cost after iteration 20: 0.6513182520866394 | Train Acc: 68.44261932373047 | Test Acc: 54.24836730957031\n",
+      "Cost after iteration 30: 0.6367825269699097 | Train Acc: 68.03278350830078 | Test Acc: 54.24836730957031\n",
+      "Cost after iteration 40: 0.6245337128639221 | Train Acc: 69.67213439941406 | Test Acc: 54.90196228027344\n",
+      "Cost after iteration 50: 0.6139225363731384 | Train Acc: 70.90164184570312 | Test Acc: 56.20914840698242\n",
+      "Cost after iteration 60: 0.6045235395431519 | Train Acc: 72.54098510742188 | Test Acc: 56.86274337768555\n",
+      "Cost after iteration 70: 0.5960512161254883 | Train Acc: 74.18032836914062 | Test Acc: 57.51633834838867\n",
+      "Cost after iteration 80: 0.5883084535598755 | Train Acc: 73.77049255371094 | Test Acc: 57.51633834838867\n",
+      "Cost after iteration 90: 0.5811557769775391 | Train Acc: 74.59016418457031 | Test Acc: 58.1699333190918\n"
+     ]
+    }
+   ],
+   "source": [
+    "# training the model\n",
+    "costs = []\n",
+    "\n",
+    "for ITER in range(100):\n",
+    "    lrmodel.train()\n",
+    "    x, y = next(iter(train_dataset))\n",
+    "    test_x, test_y = next(iter(test_dataset))\n",
+    "\n",
+    "    # forward\n",
+    "    yhat = lrmodel.forward(x.to(device))\n",
+    "\n",
+    "    cost = criterion(yhat.squeeze(), y.type(torch.FloatTensor).to(device))\n",
+    "    train_pred = predict(yhat, y)\n",
+    "\n",
+    "    # backward\n",
+    "    optimizer.zero_grad()\n",
+    "    cost.backward()\n",
+    "    optimizer.step()\n",
+    "    \n",
+    "    # evaluate\n",
+    "    lrmodel.eval()\n",
+    "    with torch.no_grad():\n",
+    "        yhat_test = lrmodel.forward(test_x.to(device))\n",
+    "        test_pred = predict(yhat_test, test_y)\n",
+    "\n",
+    "    if ITER % 10 == 0:\n",
+    "        costs.append(cost)\n",
+    "\n",
+    "    if ITER % 10 == 0:\n",
+    "        print(\"Cost after iteration {}: {} | Train Acc: {} | Test Acc: {}\".format(ITER, \n",
+    "                                                                                    cost, \n",
+    "                                                                                    train_pred,\n",
+    "                                                                                    test_pred))\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "- [A Logistic Regression Model from Scratch](https://colab.research.google.com/drive/1iBoJ0kngkOthy7SgVaVQA1aHEROt5mra?usp=sharing)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('play')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "cf9800998463bc980d70cdbacff0c7e9a10687346dc898569e92f016d6e252c9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/deep_cbow.ipynb b/notebooks/deep_cbow.ipynb
new file mode 100644
index 0000000..02a63aa
--- /dev/null
+++ b/notebooks/deep_cbow.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deep Continuous Bag of Words (Deep CBOW) Text Classifier\n",
+    "\n",
+    "The code below implements a continuous bag of words text classifier.\n",
+    "- We tokenize the text, create a vocabulary and encode each piece of text in the dataset\n",
+    "- We create embeddings for inputs and sum them together\n",
+    "- The resulting vector is fed to hidden neural network, which generates a new vector that is multiplied to a weights matrix\n",
+    "- We then add the bias and obtain scores\n",
+    "- The scores are applied a softmax to generate probabilities which are used for the final classification\n",
+    "\n",
+    "The code used in this notebook was inspired by code from the [official repo](https://github.com/neubig/nn4nlp-code) used in the [CMU Neural Networks for NLP class](http://www.phontron.com/class/nn4nlp2021/schedule.html) by [Graham Neubig](http://www.phontron.com/index.php). \n",
+    "\n",
+    "![img txt](https://github.com/dair-ai/ML-Notebooks/blob/main/img/deep_cbow.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "''' uncomment to download the data\n",
+    "%%capture\n",
+    "\n",
+    "# download the files\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/dev.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/test.txt\n",
+    "!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/classes/train.txt\n",
+    "\n",
+    "# create the data folders\n",
+    "!mkdir data data/classes\n",
+    "!cp dev.txt data/classes\n",
+    "!cp test.txt data/classes\n",
+    "!cp train.txt data/classes\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read and Process the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, process each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, 'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.lower().strip()\n",
+    "            line = line.split(' ||| ')\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "train_data = read_data('data/classes/train.txt')\n",
+    "test_data = read_data('data/classes/test.txt')\n",
+    "\n",
+    "# creating the word and tag indices\n",
+    "word_to_index = {}\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # add <UNK> to dictionary\n",
+    "tag_to_index = {}\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line[1].split(\" \"):\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "            else:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "\n",
+    "        if line[0] not in tag_to_index:\n",
+    "            tag_to_index[line[0]] = len(tag_to_index)\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(test_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield([word_to_index[word] for word in line[1].split(\" \")], tag_to_index[line[0]])\n",
+    "\n",
+    "train_data = list(create_tensor(train_data))\n",
+    "test_data = list(create_tensor(test_data))\n",
+    "\n",
+    "number_of_words = len(word_to_index)\n",
+    "number_of_tags = len(tag_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "# create a simple neural network with embedding layer, bias, and xavier initialization\n",
+    "class DeepCBoW(nn.Module):\n",
+    "    def __init__(self, nwords, ntags, hidden_size, num_layers, emb_size):\n",
+    "        super(DeepCBoW, self).__init__()\n",
+    "\n",
+    "        self.num_layers = num_layers\n",
+    "\n",
+    "        # layers\n",
+    "        self.embedding = nn.Embedding(nwords, emb_size)\n",
+    "        self.linears = nn.ModuleList([nn.Linear(emb_size if i ==0 else hidden_size, hidden_size) \\\n",
+    "            for i in range(num_layers)])\n",
+    "\n",
+    "        # use xavier initialization for weights\n",
+    "        nn.init.xavier_uniform_(self.embedding.weight)\n",
+    "        for i in range(self.num_layers):\n",
+    "            nn.init.xavier_uniform_(self.linears[i].weight)\n",
+    "\n",
+    "        # output layer\n",
+    "        self.output_layer = nn.Linear(hidden_size, ntags)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        emb = self.embedding(x) # seq x emb_size\n",
+    "        emb_sum = torch.sum(emb, dim=0) # emb_size\n",
+    "        h = emb_sum.view(1, -1) # reshape to (1, emb_size)\n",
+    "        for i in range(self.num_layers):\n",
+    "            h = torch.tanh(self.linears[i](h))\n",
+    "        out = self.output_layer(h) # 1 x ntags\n",
+    "        return out\n",
+    "\n",
+    "HIDDEN_SIZE = 64\n",
+    "NUM_LAYERS = 2 # hidden layers\n",
+    "EMB_SIZE = 64\n",
+    "model = DeepCBoW(number_of_words, number_of_tags, HIDDEN_SIZE, NUM_LAYERS, EMB_SIZE).to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters())\n",
+    "type = torch.LongTensor\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "    type = torch.cuda.LongTensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 | train loss/sent: 1.4293 | train accuracy: 0.3765 | test accuracy: 0.3941\n",
+      "epoch: 2 | train loss/sent: 1.0343 | train accuracy: 0.5729 | test accuracy: 0.4127\n",
+      "epoch: 3 | train loss/sent: 0.6565 | train accuracy: 0.7583 | test accuracy: 0.3801\n",
+      "epoch: 4 | train loss/sent: 0.4013 | train accuracy: 0.8586 | test accuracy: 0.3783\n",
+      "epoch: 5 | train loss/sent: 0.2659 | train accuracy: 0.9079 | test accuracy: 0.3959\n",
+      "epoch: 6 | train loss/sent: 0.1747 | train accuracy: 0.9419 | test accuracy: 0.3787\n",
+      "epoch: 7 | train loss/sent: 0.1257 | train accuracy: 0.9573 | test accuracy: 0.3805\n",
+      "epoch: 8 | train loss/sent: 0.0860 | train accuracy: 0.9702 | test accuracy: 0.3719\n",
+      "epoch: 9 | train loss/sent: 0.0652 | train accuracy: 0.9768 | test accuracy: 0.3747\n",
+      "epoch: 10 | train loss/sent: 0.0434 | train accuracy: 0.9860 | test accuracy: 0.3887\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    }
+   ],
+   "source": [
+    "# perform training of the Bow model\n",
+    "\n",
+    "for epoch in range(10):\n",
+    "    # perform training\n",
+    "    model.train()\n",
+    "    random.shuffle(train_data)\n",
+    "    total_loss = 0.0\n",
+    "    train_correct = 0\n",
+    "    for sentence, tag in train_data:\n",
+    "        sentence = torch.tensor(sentence).type(type)\n",
+    "        tag = torch.tensor([tag]).type(type)\n",
+    "        output = model(sentence)\n",
+    "        predicted = torch.argmax(output.data.detach()).item()\n",
+    "        \n",
+    "        loss = criterion(output, tag)\n",
+    "        total_loss += loss.item()\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if predicted == tag: train_correct+=1\n",
+    "\n",
+    "    # perform testing of the model\n",
+    "    model.eval()\n",
+    "    test_correct = 0\n",
+    "    for sentence, tag in test_data:\n",
+    "        sentence = torch.tensor(sentence).type(type)\n",
+    "        output = model(sentence)\n",
+    "        predicted = torch.argmax(output.data.detach()).item()\n",
+    "        if predicted == tag: test_correct += 1\n",
+    "    \n",
+    "    # print model performance results\n",
+    "    log = f'epoch: {epoch+1} | ' \\\n",
+    "        f'train loss/sent: {total_loss/len(train_data):.4f} | ' \\\n",
+    "        f'train accuracy: {train_correct/len(train_data):.4f} | ' \\\n",
+    "        f'test accuracy: {test_correct/len(test_data):.4f}'\n",
+    "    \n",
+    "    print(log)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12 (main, Apr  5 2022, 06:56:58) \n[GCC 7.5.0]"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/first_nn.ipynb b/notebooks/first_nn.ipynb
new file mode 100644
index 0000000..4644ac3
--- /dev/null
+++ b/notebooks/first_nn.ipynb
@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## First Neural Network: Image Classification \n",
+    "\n",
+    "Objectives:\n",
+    "- Train a minimal image classifier on [MNIST](https://paperswithcode.com/dataset/mnist) using PyTorch\n",
+    "- Usese PyTorch and torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The usual imports\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torchvision\n",
+    "import torchvision.transforms as transforms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the data\n",
+    "\n",
+    "class ReshapeTransform:\n",
+    "    def __init__(self, new_size):\n",
+    "        self.new_size = new_size\n",
+    "\n",
+    "    def __call__(self, img):\n",
+    "        return torch.reshape(img, self.new_size)\n",
+    "\n",
+    "transformations = transforms.Compose([\n",
+    "                                transforms.ToTensor(),\n",
+    "                                transforms.ConvertImageDtype(torch.float32),\n",
+    "                                ReshapeTransform((-1,))\n",
+    "                                ])\n",
+    "\n",
+    "trainset = torchvision.datasets.MNIST(root='./data', train=True,\n",
+    "                                        download=True, transform=transformations)\n",
+    "\n",
+    "testset = torchvision.datasets.MNIST(root='./data', train=False,\n",
+    "                                       download=True, transform=transformations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([60000, 28, 28]), torch.Size([10000, 28, 28]))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check shape of data\n",
+    "\n",
+    "trainset.data.shape, testset.data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data loader\n",
+    "\n",
+    "BATCH_SIZE = 128\n",
+    "train_dataloader = torch.utils.data.DataLoader(trainset, \n",
+    "                                               batch_size=BATCH_SIZE,\n",
+    "                                               shuffle=True, \n",
+    "                                               num_workers=0)\n",
+    "\n",
+    "test_dataloader = torch.utils.data.DataLoader(testset, \n",
+    "                                              batch_size=BATCH_SIZE,\n",
+    "                                              shuffle=False, \n",
+    "                                              num_workers=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model\n",
+    "\n",
+    "model = nn.Sequential(nn.Linear(784, 512), nn.ReLU(), nn.Linear(512, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# training preparation\n",
+    "\n",
+    "trainer = torch.optim.RMSprop(model.parameters())\n",
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_accuracy(output, target, batch_size):\n",
+    "    # Obtain accuracy for training round\n",
+    "    corrects = (torch.max(output, 1)[1].view(target.size()).data == target.data).sum()\n",
+    "    accuracy = 100.0 * corrects/batch_size\n",
+    "    return accuracy.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 1 | Train loss: 0.9943 | Train Accuracy: 91.7344\n",
+      "Epoch: 2 | Train loss: 0.1334 | Train Accuracy: 95.9422\n",
+      "Epoch: 3 | Train loss: 0.1030 | Train Accuracy: 96.8767\n",
+      "Epoch: 4 | Train loss: 0.0845 | Train Accuracy: 97.4997\n",
+      "Epoch: 5 | Train loss: 0.0735 | Train Accuracy: 97.8811\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train\n",
+    "\n",
+    "for ITER in range(5):\n",
+    "    train_acc = 0.0\n",
+    "    train_running_loss = 0.0\n",
+    "\n",
+    "    model.train()\n",
+    "    for i, (X, y) in enumerate(train_dataloader):\n",
+    "        output = model(X)\n",
+    "        l = loss(output, y)\n",
+    "\n",
+    "        # update the parameters\n",
+    "        l.backward()\n",
+    "        trainer.step()\n",
+    "        trainer.zero_grad()\n",
+    "\n",
+    "        # gather metrics\n",
+    "        train_acc += get_accuracy(output, y, BATCH_SIZE)\n",
+    "        train_running_loss += l.detach().item()\n",
+    "\n",
+    "    print('Epoch: %d | Train loss: %.4f | Train Accuracy: %.4f' \\\n",
+    "          %(ITER+1, train_running_loss / (i+1),train_acc/(i+1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Other things to try\n",
+    "\n",
+    "- Evaluate on test set\n",
+    "- Plot loss curve\n",
+    "- Add more layers to the model"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('play')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "cf9800998463bc980d70cdbacff0c7e9a10687346dc898569e92f016d6e252c9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/intro_gnn.ipynb b/notebooks/intro_gnn.ipynb
new file mode 100644
index 0000000..e27e725
--- /dev/null
+++ b/notebooks/intro_gnn.ipynb
@@ -0,0 +1,817 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to GNNs with PyTorch Geometric\n",
+    "\n",
+    "In this short notebook, the goal is to provide a introductory guide to get started with Graph Neural Networks using the popular library called [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/index.html). PyTorch Geometric is a PyTorch based libary hence we will be using PyTorch in this tutorial. \n",
+    "\n",
+    "The code used in this tutorial has been adapted from their official [examples](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html). I have incorporated a bit more beginner-friendly guidance and kept it minimal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find the CUDA version PyTorch was installed with\n",
+    "!python -c \"import torch; print(torch.version.cuda)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.11.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# PyTorch version\n",
+    "!python -c \"import torch; print(torch.__version__)\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the follow packages but make sure to \n",
+    "\n",
+    "---\n",
+    "\n",
+    "install the right version below. Find more instructions [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) if you get lost. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in links: https://data.pyg.org/whl/torch-1.11.0.html\n",
+      "Collecting torch-scatter\n",
+      "  Downloading torch_scatter-2.0.9.tar.gz (21 kB)\n",
+      "Building wheels for collected packages: torch-scatter\n",
+      "  Building wheel for torch-scatter (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for torch-scatter: filename=torch_scatter-2.0.9-cp38-cp38-linux_x86_64.whl size=304063 sha256=f9cc42222b3244636f8cb1a257bdc1556f9292207d987b02ec79e7fb59689fdb\n",
+      "  Stored in directory: /home/codespace/.cache/pip/wheels/7c/51/2a/409339f45a48bf748a5db76dfa11373ea7c883ecf1932eee2f\n",
+      "Successfully built torch-scatter\n",
+      "Installing collected packages: torch-scatter\n",
+      "Successfully installed torch-scatter-2.0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install torch-scatter -f https://data.pyg.org/whl/torch-1.11.0.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in links: https://data.pyg.org/whl/torch-1.11.0.html\n",
+      "Collecting torch-sparse\n",
+      "  Downloading torch_sparse-0.6.13.tar.gz (48 kB)\n",
+      "\u001b[K     |████████████████████████████████| 48 kB 1.7 MB/s  eta 0:00:01\n",
+      "\u001b[?25hCollecting scipy\n",
+      "  Downloading scipy-1.8.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 41.6 MB 73.5 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: numpy<1.25.0,>=1.17.3 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from scipy->torch-sparse) (1.22.3)\n",
+      "Building wheels for collected packages: torch-sparse\n",
+      "  Building wheel for torch-sparse (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for torch-sparse: filename=torch_sparse-0.6.13-cp38-cp38-linux_x86_64.whl size=570934 sha256=27a158cb7be5e10bb7846cce70764338cbb12828ea70eaf5a41a79f15cfc60c3\n",
+      "  Stored in directory: /home/codespace/.cache/pip/wheels/81/94/1a/3fd0c022a887c997c5e681961f2bd2e41f8fd6b66562b90fb6\n",
+      "Successfully built torch-sparse\n",
+      "Installing collected packages: scipy, torch-sparse\n",
+      "Successfully installed scipy-1.8.1 torch-sparse-0.6.13\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install torch-sparse -f https://data.pyg.org/whl/torch-1.11.0.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting torch-geometric\n",
+      "  Downloading torch_geometric-2.0.4.tar.gz (407 kB)\n",
+      "\u001b[K     |████████████████████████████████| 407 kB 26.9 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting tqdm\n",
+      "  Downloading tqdm-4.64.0-py2.py3-none-any.whl (78 kB)\n",
+      "\u001b[K     |████████████████████████████████| 78 kB 1.8 MB/s  eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: numpy in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from torch-geometric) (1.22.3)\n",
+      "Requirement already satisfied: scipy in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from torch-geometric) (1.8.1)\n",
+      "Collecting pandas\n",
+      "  Downloading pandas-1.4.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.7 MB)\n",
+      "\u001b[K     |████████████████████████████████| 11.7 MB 79.4 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting jinja2\n",
+      "  Downloading Jinja2-3.1.2-py3-none-any.whl (133 kB)\n",
+      "\u001b[K     |████████████████████████████████| 133 kB 87.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: requests in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from torch-geometric) (2.27.1)\n",
+      "Collecting pyparsing\n",
+      "  Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)\n",
+      "Collecting scikit-learn\n",
+      "  Downloading scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (31.2 MB)\n",
+      "\u001b[K     |████████████████████████████████| 31.2 MB 84.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting MarkupSafe>=2.0\n",
+      "  Downloading MarkupSafe-2.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\n",
+      "Collecting pytz>=2020.1\n",
+      "  Downloading pytz-2022.1-py2.py3-none-any.whl (503 kB)\n",
+      "\u001b[K     |████████████████████████████████| 503 kB 69.2 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from pandas->torch-geometric) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas->torch-geometric) (1.16.0)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from requests->torch-geometric) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from requests->torch-geometric) (3.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from requests->torch-geometric) (2022.5.18.1)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/codespace/.conda/envs/gnn/lib/python3.8/site-packages (from requests->torch-geometric) (1.26.9)\n",
+      "Collecting joblib>=1.0.0\n",
+      "  Downloading joblib-1.1.0-py2.py3-none-any.whl (306 kB)\n",
+      "\u001b[K     |████████████████████████████████| 306 kB 78.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting threadpoolctl>=2.0.0\n",
+      "  Downloading threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
+      "Building wheels for collected packages: torch-geometric\n",
+      "  Building wheel for torch-geometric (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for torch-geometric: filename=torch_geometric-2.0.4-py3-none-any.whl size=616602 sha256=c3dd839f53e4c307e7a223cb3868bbe2dfc5bb5a5586c2f1bcfe5d2f20f6d6a6\n",
+      "  Stored in directory: /home/codespace/.cache/pip/wheels/c1/be/e9/b90ded2a496c975a539af002fe1f0f2a22a97af13b41866d6e\n",
+      "Successfully built torch-geometric\n",
+      "Installing collected packages: threadpoolctl, pytz, MarkupSafe, joblib, tqdm, scikit-learn, pyparsing, pandas, jinja2, torch-geometric\n",
+      "Successfully installed MarkupSafe-2.1.1 jinja2-3.1.2 joblib-1.1.0 pandas-1.4.2 pyparsing-3.0.9 pytz-2022.1 scikit-learn-1.1.1 threadpoolctl-3.1.0 torch-geometric-2.0.4 tqdm-4.64.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install torch-geometric"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Started\n",
+    "\n",
+    "Import PyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.11.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# print torch version\n",
+    "print(torch.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The great thing about PyTorch Geometric is that it contain useful functionalities to import and load graph related data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch_geometric.data import Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's create an unweighted and undirected graph with three nodes and four total edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data(x=[3, 1], edge_index=[2, 4])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define edge list\n",
+    "edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)\n",
+    "\n",
+    "# define node features\n",
+    "x = torch.tensor([[-1], [0], [1]])\n",
+    "\n",
+    "# create graph data object\n",
+    "data = Data(x=x, edge_index=edge_index)\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our data object `Data` has many useful utility functions to check the properties of the graph. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check number of edges of the graph\n",
+    "print(data.num_edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check number of nodes of the graph\n",
+    "print(data.num_nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check number of features of the graph\n",
+    "print(data.num_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check if graph is directed\n",
+    "print(data.is_directed())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data\n",
+    "\n",
+    "Find more fun functions related to graph data [here](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data). \n",
+    "\n",
+    "One of the cool things about the PyTorch Geometric library is that it contains out-of-the-box benchmark datasets that are ready to use and explore. A popular dataset is the Cora dataset that is used for supervised graph node classification. (We will talk about these applications in an upcoming tutorial but for now we will focus on the data itself).\n",
+    "\n",
+    "\"The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words.\" - [Papers with Code](https://paperswithcode.com/dataset/cora).\n",
+    "\n",
+    "Let's load the Cora dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph\n",
+      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index\n",
+      "Processing...\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch_geometric.datasets import Planetoid\n",
+    "\n",
+    "dataset = Planetoid(root='tmp/Cora', name='Cora')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check some of the properties of the Cora dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of graphs:  1\n",
+      "Number of features:  1433\n",
+      "Number of classes:  7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# number of graphs\n",
+    "print(\"Number of graphs: \", len(dataset))\n",
+    "\n",
+    "# number of features\n",
+    "print(\"Number of features: \", dataset.num_features)\n",
+    "\n",
+    "# number of classes\n",
+    "print(\"Number of classes: \", dataset.num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that this particular graph dataset only contains one graph. Graph data can be very complex and can include multiple graphs depending on the type of data and application. Let's check more feature of the Cora dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of nodes:  2708\n",
+      "Number of edges:  10556\n",
+      "Is directed:  False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# select the first graph\n",
+    "data = dataset[0]\n",
+    "\n",
+    "# number of nodes\n",
+    "print(\"Number of nodes: \", data.num_nodes)\n",
+    "\n",
+    "# number of edges\n",
+    "print(\"Number of edges: \", data.num_edges)\n",
+    "\n",
+    "# check if directed\n",
+    "print(\"Is directed: \", data.is_directed())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can sample nodes from the graph this way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of sample nodes:  torch.Size([5, 1433])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sample nodes from the graph\n",
+    "print(\"Shape of sample nodes: \", data.x[:5].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We extracted 5 nodes from the graph and checked its shape. You will see that each node has `1433` features.\n",
+    "\n",
+    "Another great advantage of using PyTorch Geometric to load the Cora data is that it comes pre-processed and ready to use. It also has the splits for training, validation and test which we can directly use for training a GNN.\n",
+    "\n",
+    "Let's check some stats for the partitions of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# of nodes to train on:  140\n",
+      "# of nodes to test on:  1000\n",
+      "# of nodes to validate on:  500\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check training nodes\n",
+    "print(\"# of nodes to train on: \", data.train_mask.sum().item())\n",
+    "\n",
+    "# check test nodes\n",
+    "print(\"# of nodes to test on: \", data.test_mask.sum().item())\n",
+    "\n",
+    "# check validation nodes\n",
+    "print(\"# of nodes to validate on: \", data.val_mask.sum().item())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That information is important as it will indicate to our model which nodes to train against and which to test against, and so on.\n",
+    "\n",
+    "When training neural networks we train them using batches of data. PyTorch Geometric provides efficient processes to load batches of data.\n",
+    "\n",
+    "PyTorch Geometric contains a data loader which is a very popular feature in PyTorch to efficiently load data when training neural networks.\n",
+    " \n",
+    "So let's try to load the data using the built in `DataLoader`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch_geometric.datasets import Planetoid\n",
+    "from torch_geometric.loader import DataLoader\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = Planetoid(root='tmp/Cora', name='Cora')\n",
+    "data = dataset[0].to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print some quick statistics about the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape:  torch.Size([2708, 1433])\n",
+      "Edge shape:  torch.Size([2, 10556])\n",
+      "Y shape:  torch.Size([2708])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"X shape: \", data.x.shape)\n",
+    "print(\"Edge shape: \", data.edge_index.shape)\n",
+    "print(\"Y shape: \", data.y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model and Training\n",
+    "\n",
+    "Finally, let's define a standard GCN to train on the Cora dataset. The aim is to train a model that gets better at predicting the class of the node.\n",
+    "\n",
+    "To keep thins simple we will use the same model definition as used in the [tutorial](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html) we adpated the code from. Note that we are using the built-in `GCNConv` model but you could easily implement your own (something we will cover in a future tutorial). \n",
+    "\n",
+    "The model below uses two `GCNConv` layers. The first layer is followed by a non-linearity `ReLU` and `Dropout`. The result is fed to the second layer on top of which we apply `Softmax` to get distribution over the number of classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "from torch_geometric.nn import GCNConv\n",
+    "\n",
+    "class GCN(torch.nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        \"\"\" GCNConv layers \"\"\"\n",
+    "        self.conv1 = GCNConv(data.num_features, 16)\n",
+    "        self.conv2 = GCNConv(16, dataset.num_classes)\n",
+    "\n",
+    "    def forward(self, data):\n",
+    "        x, edge_index = data.x, data.edge_index\n",
+    "        x = self.conv1(x, edge_index)\n",
+    "        x = F.relu(x)\n",
+    "        x = F.dropout(x, training=self.training)\n",
+    "        x = self.conv2(x, edge_index)\n",
+    "\n",
+    "        return F.log_softmax(x, dim=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initial model and optimizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = GCN().to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define accuracy function for evaluating performance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# useful function for computing accuracy\n",
+    "def compute_accuracy(pred_y, y):\n",
+    "    return (pred_y == y).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finally we train the model on the trainin nodes for 200 epochs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 10, Loss: 0.8056, Training Acc: 0.9214\n",
+      "Epoch: 20, Loss: 0.2925, Training Acc: 0.9500\n",
+      "Epoch: 30, Loss: 0.1065, Training Acc: 1.0000\n",
+      "Epoch: 40, Loss: 0.0654, Training Acc: 1.0000\n",
+      "Epoch: 50, Loss: 0.0423, Training Acc: 1.0000\n",
+      "Epoch: 60, Loss: 0.0467, Training Acc: 0.9929\n",
+      "Epoch: 70, Loss: 0.0496, Training Acc: 0.9929\n",
+      "Epoch: 80, Loss: 0.0353, Training Acc: 0.9929\n",
+      "Epoch: 90, Loss: 0.0397, Training Acc: 1.0000\n",
+      "Epoch: 100, Loss: 0.0253, Training Acc: 1.0000\n",
+      "Epoch: 110, Loss: 0.0353, Training Acc: 0.9929\n",
+      "Epoch: 120, Loss: 0.0340, Training Acc: 1.0000\n",
+      "Epoch: 130, Loss: 0.0338, Training Acc: 1.0000\n",
+      "Epoch: 140, Loss: 0.0319, Training Acc: 1.0000\n",
+      "Epoch: 150, Loss: 0.0469, Training Acc: 0.9929\n",
+      "Epoch: 160, Loss: 0.0260, Training Acc: 0.9929\n",
+      "Epoch: 170, Loss: 0.0392, Training Acc: 0.9857\n",
+      "Epoch: 180, Loss: 0.0470, Training Acc: 0.9929\n",
+      "Epoch: 190, Loss: 0.0267, Training Acc: 0.9929\n",
+      "Epoch: 200, Loss: 0.0221, Training Acc: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train the model\n",
+    "model.train()\n",
+    "losses = []\n",
+    "accuracies = []\n",
+    "for epoch in range(200):\n",
+    "    optimizer.zero_grad()\n",
+    "    out = model(data)\n",
+    "\n",
+    "    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])\n",
+    "    correct = compute_accuracy(out.argmax(dim=1)[data.train_mask], data.y[data.train_mask])\n",
+    "    acc = int(correct) / int(data.train_mask.sum())\n",
+    "    losses.append(loss.item())\n",
+    "    accuracies.append(acc)\n",
+    "\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    if (epoch+1) % 10 == 0:\n",
+    "        print('Epoch: {}, Loss: {:.4f}, Training Acc: {:.4f}'.format(epoch+1, loss.item(), acc))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the loss and accuracy\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(losses)\n",
+    "plt.plot(accuracies)\n",
+    "plt.legend(['Loss', 'Accuracy'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like the model achieves a very high accuracy and small loss on the training dataset. To see how well it generalizes, let's test on the testing nodes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7870\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate the model on test set\n",
+    "model.eval()\n",
+    "pred = model(data).argmax(dim=1)\n",
+    "correct = compute_accuracy(pred[data.test_mask], data.y[data.test_mask])\n",
+    "acc = int(correct) / int(data.test_mask.sum())\n",
+    "print(f'Accuracy: {acc:.4f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Very cool! It seems we got a very nice accuracy for the test as well. Our model is doing okay. There are many ways you can go about trying to improve this model, but we will keep that for another time. Hopefully, with this tutorial you got a glimpse of graph data and how to use PyTorch Geometric to train GNNs on a very popular dataset. \n",
+    "\n",
+    "Note that I haven't tested if this code works with GPUs. I will leave that as an exercise for the learner. \n",
+    "\n",
+    "If you are interested in the full tutorial and more examples, visit the [PyTorch Geomtric documentation](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html) where I adapted the code from. \n",
+    "\n",
+    "Feel free to reach out on [Twitter](https://twitter.com/omarsar0) if you have any further questions."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/linear_regression.ipynb b/notebooks/linear_regression.ipynb
new file mode 100644
index 0000000..e7bc359
--- /dev/null
+++ b/notebooks/linear_regression.ipynb
@@ -0,0 +1,1376 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/drive/1br0hM79ORTVNXUpVgkV5t4o4AigGxfwk?usp=sharing\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg/" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Linear Regression from Scratch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m41136O7L5bV"
+      },
+      "source": [
+        "In this tutorial, we are going to implement a linear regression model to predict california housing prices. We will build the model from scratch using numpy. This will be a great approach to begin understanding regression based models.\n",
+        "\n",
+        "After completing this tutorial the learner is expected to know the basic building blocks of a linear regression model. The learner is also expected to know the pipeline of reading and transforming data for machine learning workflows.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "Pni17h4R8v8a"
+      },
+      "outputs": [],
+      "source": [
+        "## Import the usual libraries\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn.datasets import fetch_california_housing\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DjyJUfczL4zX"
+      },
+      "source": [
+        "# Importing the dataset\n",
+        "\n",
+        "The real-world dataset can be obtained by the function `fetch_california_housing` that downloads the dataset for us. \n",
+        "\n",
+        "The `as_frame` parameter returns a pandas dataframe which is a library useful for viewing contents of the data.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "aOXxbywahC5X",
+        "outputId": "24521e0a-6f1a-4e5c-c35d-3abb8112a9af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ce5979d0-431a-47c0-a6fb-4bbe7a0e7397\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>MedInc</th>\n",
+              "      <th>HouseAge</th>\n",
+              "      <th>AveRooms</th>\n",
+              "      <th>AveBedrms</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>AveOccup</th>\n",
+              "      <th>Latitude</th>\n",
+              "      <th>Longitude</th>\n",
+              "      <th>MedHouseVal</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8.3252</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>6.984127</td>\n",
+              "      <td>1.023810</td>\n",
+              "      <td>322.0</td>\n",
+              "      <td>2.555556</td>\n",
+              "      <td>37.88</td>\n",
+              "      <td>-122.23</td>\n",
+              "      <td>4.526</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>8.3014</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>6.238137</td>\n",
+              "      <td>0.971880</td>\n",
+              "      <td>2401.0</td>\n",
+              "      <td>2.109842</td>\n",
+              "      <td>37.86</td>\n",
+              "      <td>-122.22</td>\n",
+              "      <td>3.585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.2574</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>8.288136</td>\n",
+              "      <td>1.073446</td>\n",
+              "      <td>496.0</td>\n",
+              "      <td>2.802260</td>\n",
+              "      <td>37.85</td>\n",
+              "      <td>-122.24</td>\n",
+              "      <td>3.521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>5.6431</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>5.817352</td>\n",
+              "      <td>1.073059</td>\n",
+              "      <td>558.0</td>\n",
+              "      <td>2.547945</td>\n",
+              "      <td>37.85</td>\n",
+              "      <td>-122.25</td>\n",
+              "      <td>3.413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>3.8462</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>6.281853</td>\n",
+              "      <td>1.081081</td>\n",
+              "      <td>565.0</td>\n",
+              "      <td>2.181467</td>\n",
+              "      <td>37.85</td>\n",
+              "      <td>-122.25</td>\n",
+              "      <td>3.422</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce5979d0-431a-47c0-a6fb-4bbe7a0e7397')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg/" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-ce5979d0-431a-47c0-a6fb-4bbe7a0e7397 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-ce5979d0-431a-47c0-a6fb-4bbe7a0e7397');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
+              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
+              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
+              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
+              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
+              "\n",
+              "   Longitude  MedHouseVal  \n",
+              "0    -122.23        4.526  \n",
+              "1    -122.22        3.585  \n",
+              "2    -122.24        3.521  \n",
+              "3    -122.25        3.413  \n",
+              "4    -122.25        3.422  "
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Fetch the data using sklearn function\n",
+        "bunch = fetch_california_housing(download_if_missing=True, as_frame=True)\n",
+        "\n",
+        "# Load the dataframe and view\n",
+        "df = bunch.frame\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KUeU_jLylTx7"
+      },
+      "source": [
+        "For this dataset, our target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000).\n",
+        "\n",
+        "We can take a closer look at the various statistical parameters of the dataset using pandas. The `describe` function will help us."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "eD4BpBHClgDc",
+        "outputId": "e2171ffb-bd38-4e3e-b600-ad3c249a3234"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bb12ca59-11cc-470b-aa86-ac3f6416cbf8\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>MedInc</th>\n",
+              "      <th>HouseAge</th>\n",
+              "      <th>AveRooms</th>\n",
+              "      <th>AveBedrms</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>AveOccup</th>\n",
+              "      <th>Latitude</th>\n",
+              "      <th>Longitude</th>\n",
+              "      <th>MedHouseVal</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "      <td>20640.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>3.870671</td>\n",
+              "      <td>28.639486</td>\n",
+              "      <td>5.429000</td>\n",
+              "      <td>1.096675</td>\n",
+              "      <td>1425.476744</td>\n",
+              "      <td>3.070655</td>\n",
+              "      <td>35.631861</td>\n",
+              "      <td>-119.569704</td>\n",
+              "      <td>2.068558</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.899822</td>\n",
+              "      <td>12.585558</td>\n",
+              "      <td>2.474173</td>\n",
+              "      <td>0.473911</td>\n",
+              "      <td>1132.462122</td>\n",
+              "      <td>10.386050</td>\n",
+              "      <td>2.135952</td>\n",
+              "      <td>2.003532</td>\n",
+              "      <td>1.153956</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.499900</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.846154</td>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.692308</td>\n",
+              "      <td>32.540000</td>\n",
+              "      <td>-124.350000</td>\n",
+              "      <td>0.149990</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2.563400</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>4.440716</td>\n",
+              "      <td>1.006079</td>\n",
+              "      <td>787.000000</td>\n",
+              "      <td>2.429741</td>\n",
+              "      <td>33.930000</td>\n",
+              "      <td>-121.800000</td>\n",
+              "      <td>1.196000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.534800</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>5.229129</td>\n",
+              "      <td>1.048780</td>\n",
+              "      <td>1166.000000</td>\n",
+              "      <td>2.818116</td>\n",
+              "      <td>34.260000</td>\n",
+              "      <td>-118.490000</td>\n",
+              "      <td>1.797000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>4.743250</td>\n",
+              "      <td>37.000000</td>\n",
+              "      <td>6.052381</td>\n",
+              "      <td>1.099526</td>\n",
+              "      <td>1725.000000</td>\n",
+              "      <td>3.282261</td>\n",
+              "      <td>37.710000</td>\n",
+              "      <td>-118.010000</td>\n",
+              "      <td>2.647250</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>15.000100</td>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>141.909091</td>\n",
+              "      <td>34.066667</td>\n",
+              "      <td>35682.000000</td>\n",
+              "      <td>1243.333333</td>\n",
+              "      <td>41.950000</td>\n",
+              "      <td>-114.310000</td>\n",
+              "      <td>5.000010</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb12ca59-11cc-470b-aa86-ac3f6416cbf8')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-bb12ca59-11cc-470b-aa86-ac3f6416cbf8 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-bb12ca59-11cc-470b-aa86-ac3f6416cbf8');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
+              "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
+              "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
+              "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
+              "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
+              "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
+              "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
+              "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
+              "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
+              "\n",
+              "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
+              "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
+              "mean       3.070655     35.631861   -119.569704      2.068558  \n",
+              "std       10.386050      2.135952      2.003532      1.153956  \n",
+              "min        0.692308     32.540000   -124.350000      0.149990  \n",
+              "25%        2.429741     33.930000   -121.800000      1.196000  \n",
+              "50%        2.818116     34.260000   -118.490000      1.797000  \n",
+              "75%        3.282261     37.710000   -118.010000      2.647250  \n",
+              "max     1243.333333     41.950000   -114.310000      5.000010  "
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6S6WG0Bejxc2"
+      },
+      "source": [
+        "As we can see the data in each of the columns is on different scales. For example, the average bedroom value is around 1 and the average population is around 1425. \n",
+        "\n",
+        "Generally, machine learing models do not work well when the data is on different scales. Thus, we have to normalize our data in the range [-1,1]. The module [StandardScalar](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) will help us in this.\n",
+        "\n",
+        "The training data should always be normalized. The testing data should be normalized using the values of the training data. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "pkaOgN44iQLN"
+      },
+      "outputs": [],
+      "source": [
+        "# !wget https://raw.githubusercontent.com/Ankit152/Fish-Market/main/Fish.csv\n",
+        "# import pandas as pd\n",
+        "# df  = pd.read_csv(\"Fish.csv\")\n",
+        "# y = df['Weight']\n",
+        "# x = df[[\"Length1\", \"Length2\", \"Length3\", \"Height\", \"Width\",\"Weight\"]]\n",
+        "\n",
+        "df = bunch.frame\n",
+        "x = df.iloc[:,:-1] # Select all the columns, except the last column\n",
+        "y = df.iloc[:,-1:] # Select the last column\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.33, random_state = 1)\n",
+        "\n",
+        "input_scalar = StandardScaler()\n",
+        "output_scalar = StandardScaler()\n",
+        "\n",
+        "x_train = input_scalar.fit_transform(x_train).T # Normalize train data\n",
+        "x_test = input_scalar.transform(x_test).T # Only transform test data using values of train data\n",
+        "\n",
+        "y_train = output_scalar.fit_transform(y_train).reshape(-1)\n",
+        "y_test = output_scalar.transform(y_test).reshape(-1)\n",
+        "\n",
+        "dataset_copy = [ x_train.copy(), x_test.copy(),  y_train.copy(),  y_test.copy()]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mylVXZDk96a2"
+      },
+      "source": [
+        "# Linear Regression Model\n",
+        "\n",
+        "Now we define our linear regression model from scratch.\n",
+        "\n",
+        "A linear regression model is of the form:\n",
+        "\n",
+        "$y = a_1 x_1 + a_2 x_2 + \\dots + a_nx_n + a_{n+1}$\n",
+        "  \n",
+        "The above can be rewritten using matrix multiplication as\n",
+        "\n",
+        "$ y = w^T x $\n",
+        "\n",
+        "where \n",
+        "\n",
+        "$ w = [a_1, a_2, \\dots,  a_n, a_{n+1}]^T $\n",
+        "\n",
+        "$ x = [x_1, x_2, \\dots,  x_n]^T $\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "iJViSowz9nah"
+      },
+      "outputs": [],
+      "source": [
+        "class LinearRegression():\n",
+        "  def __init__(self, dim, lr = 0.1):\n",
+        "    assert isinstance\n",
+        "    self.lr = lr\n",
+        "    self.w = np.zeros((dim))\n",
+        "    self.grads = {\"dw\": np.zeros((dim)) +5}\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    y = self.w.T @ x\n",
+        "    return y\n",
+        "  \n",
+        "  def backward(self, x, y_hat, y):\n",
+        "    assert y_hat.shape == y.shape\n",
+        "    self.grads[\"dw\"] = (1 / x.shape[1]) * ((y_hat - y) @ x.T).T\n",
+        "    assert self.grads[\"dw\"].shape == self.w.shape\n",
+        "    \n",
+        "    # print(self.grads[\"dw\"])\n",
+        "\n",
+        "  def optimize(self):\n",
+        "    self.w = self.w - self.lr * self.grads[\"dw\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6UOy32LoqCrL"
+      },
+      "source": [
+        "# Loss\n",
+        "\n",
+        "For linear regression, various loss functions such as the mean absolute error, mean squared error, or root mean squared error can be used.\n",
+        "\n",
+        "In this example, we will use the mean squared error (MSE) loss.\n",
+        "\n",
+        "The MSE loss is given by \n",
+        "\n",
+        "$ error = \\frac{1}{m} Σ_{i=1}^{m} (y_{true}^{i} - y_{pred}^{i})^2 $ \n",
+        "\n",
+        "where $i$ denotes the particular obseration/row in the dataset and $m$ is the total number of obserations.\n",
+        "\n",
+        "To ensure our model is correct, the loss should decrease over each epoch.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3atqq0KirwLu"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "mgNn3oGjjbxX",
+        "outputId": "2f67998e-2fa4-4c43-e091-d5180dd92029"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 0 | Train Loss 0.49999999999999983 | Test Loss 0.43712002508129305\n",
+            "Epoch 20 | Train Loss 0.2359010824314295 | Test Loss 0.2325611904818386\n",
+            "Epoch 40 | Train Loss 0.22188755162559423 | Test Loss 0.2217635127918686\n",
+            "Epoch 60 | Train Loss 0.21474640752415047 | Test Loss 0.2152613227580797\n",
+            "Epoch 80 | Train Loss 0.2095989567021037 | Test Loss 0.21037193610067245\n",
+            "Epoch 100 | Train Loss 0.20581761895152345 | Test Loss 0.20673038702760732\n",
+            "Epoch 120 | Train Loss 0.20303294882659725 | Test Loss 0.20402527473733864\n",
+            "Epoch 140 | Train Loss 0.20097918345162274 | Test Loss 0.20201418597478873\n",
+            "Epoch 160 | Train Loss 0.19946210112703647 | Test Loss 0.20051638978054404\n",
+            "Epoch 180 | Train Loss 0.19833950070910428 | Test Loss 0.19939846923590557\n",
+            "Epoch 200 | Train Loss 0.19750719045004836 | Test Loss 0.19856209982480624\n",
+            "Epoch 220 | Train Loss 0.1968887724916989 | Test Loss 0.1979347869242644\n",
+            "Epoch 240 | Train Loss 0.19642818272324772 | Test Loss 0.19746302071881933\n",
+            "Epoch 260 | Train Loss 0.19608424065206406 | Test Loss 0.19710724843033994\n",
+            "Epoch 280 | Train Loss 0.19582666642954677 | Test Loss 0.19683818623685323\n",
+            "Epoch 300 | Train Loss 0.19563316903289557 | Test Loss 0.19663411372213185\n",
+            "Epoch 320 | Train Loss 0.19548731666143612 | Test Loss 0.19647888801910168\n",
+            "Epoch 340 | Train Loss 0.19537697849050284 | Test Loss 0.19636048509166582\n",
+            "Epoch 360 | Train Loss 0.19529318388491884 | Test Loss 0.19626992725672532\n",
+            "Epoch 380 | Train Loss 0.19522928672715056 | Test Loss 0.19620049386222657\n",
+            "Epoch 400 | Train Loss 0.19518035283239818 | Test Loss 0.19614713968570663\n",
+            "Epoch 420 | Train Loss 0.19514271054504922 | Test Loss 0.1961060658296224\n",
+            "Epoch 440 | Train Loss 0.19511362075549118 | Test Loss 0.19607440266698017\n",
+            "Epoch 460 | Train Loss 0.19509103436032973 | Test Loss 0.196049975197568\n",
+            "Epoch 480 | Train Loss 0.19507341379189 | Test Loss 0.1960311290809374\n",
+            "Epoch 500 | Train Loss 0.195059601524607 | Test Loss 0.1960166013981983\n",
+            "Epoch 520 | Train Loss 0.19504872305387622 | Test Loss 0.19600542443105118\n",
+            "Epoch 540 | Train Loss 0.19504011519474806 | Test Loss 0.19599685384985982\n",
+            "Epoch 560 | Train Loss 0.19503327299733605 | Test Loss 0.19599031497728384\n",
+            "Epoch 580 | Train Loss 0.19502781036651573 | Test Loss 0.19598536246238987\n",
+            "Epoch 600 | Train Loss 0.19502343078313097 | Test Loss 0.19598164992474956\n",
+            "Epoch 620 | Train Loss 0.19501990548217418 | Test Loss 0.19597890702760307\n",
+            "Epoch 640 | Train Loss 0.19501705714492468 | Test Loss 0.1959769221005947\n",
+            "Epoch 660 | Train Loss 0.1950147476759042 | Test Loss 0.1959755289194148\n",
+            "Epoch 680 | Train Loss 0.19501286901218934 | Test Loss 0.19597459660841743\n",
+            "Epoch 700 | Train Loss 0.1950113361889526 | Test Loss 0.19597402189697258\n",
+            "Epoch 720 | Train Loss 0.1950100820879975 | Test Loss 0.19597372315589145\n",
+            "Epoch 740 | Train Loss 0.1950090534451732 | Test Loss 0.19597363578501353\n",
+            "Epoch 760 | Train Loss 0.1950082078022795 | Test Loss 0.19597370863036628\n",
+            "Epoch 780 | Train Loss 0.19500751116991094 | Test Loss 0.19597390118903935\n",
+            "Epoch 800 | Train Loss 0.19500693622732843 | Test Loss 0.19597418141927686\n",
+            "Epoch 820 | Train Loss 0.19500646092952323 | Test Loss 0.19597452401759977\n",
+            "Epoch 840 | Train Loss 0.19500606742426427 | Test Loss 0.1959749090579219\n",
+            "Epoch 860 | Train Loss 0.19500574120612196 | Test Loss 0.19597532091250944\n",
+            "Epoch 880 | Train Loss 0.19500547045245514 | Test Loss 0.19597574739336301\n",
+            "Epoch 900 | Train Loss 0.19500524549975556 | Test Loss 0.1959761790667557\n",
+            "Epoch 920 | Train Loss 0.19500505842876395 | Test Loss 0.19597660870438502\n",
+            "Epoch 940 | Train Loss 0.1950049027342804 | Test Loss 0.1959770308427642\n",
+            "Epoch 960 | Train Loss 0.19500477306123667 | Test Loss 0.1959774414287147\n",
+            "Epoch 980 | Train Loss 0.19500466499285687 | Test Loss 0.19597783753361098\n",
+            "Epoch 1000 | Train Loss 0.19500457487995757 | Test Loss 0.19597821712271776\n",
+            "Epoch 1020 | Train Loss 0.1950044997028894 | Test Loss 0.19597857886881814\n",
+            "Epoch 1040 | Train Loss 0.1950044369594934 | Test Loss 0.19597892200155245\n",
+            "Epoch 1060 | Train Loss 0.19500438457387914 | Test Loss 0.19597924618562568\n",
+            "Epoch 1080 | Train Loss 0.1950043408219371 | Test Loss 0.1959795514224043\n",
+            "Epoch 1100 | Train Loss 0.19500430427035154 | Test Loss 0.19597983797049975\n",
+            "Epoch 1120 | Train Loss 0.19500427372654458 | Test Loss 0.1959801062817876\n",
+            "Epoch 1140 | Train Loss 0.195004248197501 | Test Loss 0.19598035694999025\n",
+            "Epoch 1160 | Train Loss 0.19500422685583052 | Test Loss 0.1959805906694935\n",
+            "Epoch 1180 | Train Loss 0.1950042090117447 | Test Loss 0.19598080820250163\n",
+            "Epoch 1200 | Train Loss 0.195004194089881 | Test Loss 0.1959810103529865\n",
+            "Epoch 1220 | Train Loss 0.19500418161010774 | Test Loss 0.1959811979461702\n",
+            "Epoch 1240 | Train Loss 0.1950041711716055 | Test Loss 0.19598137181250938\n",
+            "Epoch 1260 | Train Loss 0.1950041624396518 | Test Loss 0.1959815327753366\n",
+            "Epoch 1280 | Train Loss 0.19500415513463745 | Test Loss 0.19598168164146745\n",
+            "Epoch 1300 | Train Loss 0.19500414902293145 | Test Loss 0.19598181919420496\n",
+            "Epoch 1320 | Train Loss 0.1950041439092753 | Test Loss 0.19598194618827683\n",
+            "Epoch 1340 | Train Loss 0.19500413963044858 | Test Loss 0.19598206334632265\n",
+            "Epoch 1360 | Train Loss 0.1950041360499879 | Test Loss 0.19598217135661974\n",
+            "Epoch 1380 | Train Loss 0.19500413305378356 | Test Loss 0.19598227087179027\n",
+            "Epoch 1400 | Train Loss 0.1950041305464049 | Test Loss 0.19598236250828016\n",
+            "Epoch 1420 | Train Loss 0.1950041284480337 | Test Loss 0.19598244684643956\n",
+            "Epoch 1440 | Train Loss 0.19500412669190298 | Test Loss 0.19598252443106423\n",
+            "Epoch 1460 | Train Loss 0.1950041252221582 | Test Loss 0.1959825957722857\n",
+            "Epoch 1480 | Train Loss 0.1950041239920701 | Test Loss 0.19598266134671743\n",
+            "Epoch 1500 | Train Loss 0.1950041229625413 | Test Loss 0.19598272159878383\n",
+            "Epoch 1520 | Train Loss 0.19500412210085774 | Test Loss 0.19598277694217195\n",
+            "Epoch 1540 | Train Loss 0.19500412137964543 | Test Loss 0.19598282776135909\n",
+            "Epoch 1560 | Train Loss 0.19500412077599738 | Test Loss 0.19598287441317852\n",
+            "Epoch 1580 | Train Loss 0.19500412027074418 | Test Loss 0.19598291722839395\n",
+            "Epoch 1600 | Train Loss 0.19500411984784347 | Test Loss 0.1959829565132598\n",
+            "Epoch 1620 | Train Loss 0.19500411949386964 | Test Loss 0.19598299255105023\n",
+            "Epoch 1640 | Train Loss 0.19500411919758648 | Test Loss 0.1959830256035433\n",
+            "Epoch 1660 | Train Loss 0.19500411894959 | Test Loss 0.1959830559124517\n",
+            "Epoch 1680 | Train Loss 0.19500411874200962 | Test Loss 0.19598308370079265\n",
+            "Epoch 1700 | Train Loss 0.19500411856825786 | Test Loss 0.19598310917419287\n",
+            "Epoch 1720 | Train Loss 0.19500411842282128 | Test Loss 0.1959831325221266\n",
+            "Epoch 1740 | Train Loss 0.19500411830108505 | Test Loss 0.19598315391908486\n",
+            "Epoch 1760 | Train Loss 0.19500411819918667 | Test Loss 0.19598317352567718\n",
+            "Epoch 1780 | Train Loss 0.19500411811389318 | Test Loss 0.19598319148966561\n",
+            "Epoch 1800 | Train Loss 0.19500411804249856 | Test Loss 0.19598320794693336\n",
+            "Epoch 1820 | Train Loss 0.19500411798273784 | Test Loss 0.1959832230223899\n",
+            "Epoch 1840 | Train Loss 0.19500411793271513 | Test Loss 0.19598323683081562\n",
+            "Epoch 1860 | Train Loss 0.19500411789084354 | Test Loss 0.19598324947764745\n",
+            "Epoch 1880 | Train Loss 0.19500411785579488 | Test Loss 0.19598326105971003\n",
+            "Epoch 1900 | Train Loss 0.19500411782645727 | Test Loss 0.19598327166589385\n",
+            "Epoch 1920 | Train Loss 0.19500411780190013 | Test Loss 0.1959832813777842\n",
+            "Epoch 1940 | Train Loss 0.1950041177813444 | Test Loss 0.1959832902702435\n",
+            "Epoch 1960 | Train Loss 0.1950041177641382 | Test Loss 0.1959832984119505\n",
+            "Epoch 1980 | Train Loss 0.19500411774973558 | Test Loss 0.19598330586589785\n",
+            "Epoch 2000 | Train Loss 0.1950041177376798 | Test Loss 0.1959833126898522\n",
+            "Epoch 2020 | Train Loss 0.19500411772758847 | Test Loss 0.1959833189367787\n",
+            "Epoch 2040 | Train Loss 0.19500411771914136 | Test Loss 0.19598332465523224\n",
+            "Epoch 2060 | Train Loss 0.1950041177120707 | Test Loss 0.19598332988971823\n",
+            "Epoch 2080 | Train Loss 0.1950041177061521 | Test Loss 0.1959833346810246\n",
+            "Epoch 2100 | Train Loss 0.195004117701198 | Test Loss 0.19598333906652787\n",
+            "Epoch 2120 | Train Loss 0.19500411769705106 | Test Loss 0.19598334308047438\n",
+            "Epoch 2140 | Train Loss 0.1950041176935798 | Test Loss 0.19598334675423898\n",
+            "Epoch 2160 | Train Loss 0.19500411769067422 | Test Loss 0.1959833501165632\n",
+            "Epoch 2180 | Train Loss 0.19500411768824202 | Test Loss 0.1959833531937735\n",
+            "Epoch 2200 | Train Loss 0.19500411768620615 | Test Loss 0.19598335600998243\n",
+            "Epoch 2220 | Train Loss 0.19500411768450196 | Test Loss 0.1959833585872731\n",
+            "Epoch 2240 | Train Loss 0.19500411768307552 | Test Loss 0.19598336094586813\n",
+            "Epoch 2260 | Train Loss 0.19500411768188147 | Test Loss 0.19598336310428585\n",
+            "Epoch 2280 | Train Loss 0.19500411768088202 | Test Loss 0.195983365079482\n",
+            "Epoch 2300 | Train Loss 0.19500411768004536 | Test Loss 0.19598336688698145\n",
+            "Epoch 2320 | Train Loss 0.19500411767934506 | Test Loss 0.1959833685409978\n",
+            "Epoch 2340 | Train Loss 0.19500411767875886 | Test Loss 0.19598337005454344\n",
+            "Epoch 2360 | Train Loss 0.1950041176782682 | Test Loss 0.1959833714395307\n",
+            "Epoch 2380 | Train Loss 0.1950041176778575 | Test Loss 0.1959833727068645\n",
+            "Epoch 2400 | Train Loss 0.19500411767751366 | Test Loss 0.19598337386652676\n",
+            "Epoch 2420 | Train Loss 0.19500411767722584 | Test Loss 0.1959833749276544\n",
+            "Epoch 2440 | Train Loss 0.19500411767698494 | Test Loss 0.1959833758986108\n",
+            "Epoch 2460 | Train Loss 0.19500411767678333 | Test Loss 0.19598337678705066\n",
+            "Epoch 2480 | Train Loss 0.19500411767661452 | Test Loss 0.19598337759998\n",
+            "Epoch 2500 | Train Loss 0.19500411767647324 | Test Loss 0.19598337834381113\n",
+            "Epoch 2520 | Train Loss 0.195004117676355 | Test Loss 0.19598337902441254\n",
+            "Epoch 2540 | Train Loss 0.195004117676256 | Test Loss 0.1959833796471551\n",
+            "Epoch 2560 | Train Loss 0.19500411767617312 | Test Loss 0.19598338021695402\n",
+            "Epoch 2580 | Train Loss 0.1950041176761038 | Test Loss 0.19598338073830746\n",
+            "Epoch 2600 | Train Loss 0.19500411767604572 | Test Loss 0.19598338121533196\n",
+            "Epoch 2620 | Train Loss 0.19500411767599712 | Test Loss 0.19598338165179446\n",
+            "Epoch 2640 | Train Loss 0.1950041176759564 | Test Loss 0.19598338205114224\n",
+            "Epoch 2660 | Train Loss 0.19500411767592238 | Test Loss 0.19598338241652982\n",
+            "Epoch 2680 | Train Loss 0.19500411767589387 | Test Loss 0.19598338275084387\n",
+            "Epoch 2700 | Train Loss 0.19500411767587003 | Test Loss 0.1959833830567258\n",
+            "Epoch 2720 | Train Loss 0.19500411767585005 | Test Loss 0.19598338333659285\n",
+            "Epoch 2740 | Train Loss 0.1950041176758333 | Test Loss 0.19598338359265657\n",
+            "Epoch 2760 | Train Loss 0.19500411767581935 | Test Loss 0.19598338382694094\n",
+            "Epoch 2780 | Train Loss 0.19500411767580764 | Test Loss 0.19598338404129775\n",
+            "Epoch 2800 | Train Loss 0.19500411767579784 | Test Loss 0.19598338423742154\n",
+            "Epoch 2820 | Train Loss 0.19500411767578962 | Test Loss 0.19598338441686272\n",
+            "Epoch 2840 | Train Loss 0.19500411767578277 | Test Loss 0.19598338458104\n",
+            "Epoch 2860 | Train Loss 0.195004117675777 | Test Loss 0.1959833847312515\n",
+            "Epoch 2880 | Train Loss 0.1950041176757722 | Test Loss 0.19598338486868488\n",
+            "Epoch 2900 | Train Loss 0.19500411767576814 | Test Loss 0.19598338499442705\n",
+            "Epoch 2920 | Train Loss 0.19500411767576475 | Test Loss 0.19598338510947227\n",
+            "Epoch 2940 | Train Loss 0.19500411767576192 | Test Loss 0.19598338521473044\n",
+            "Epoch 2960 | Train Loss 0.1950041176757596 | Test Loss 0.19598338531103396\n",
+            "Epoch 2980 | Train Loss 0.19500411767575762 | Test Loss 0.19598338539914462\n",
+            "Epoch 3000 | Train Loss 0.19500411767575593 | Test Loss 0.1959833854797592\n",
+            "Epoch 3020 | Train Loss 0.19500411767575457 | Test Loss 0.1959833855535154\n",
+            "Epoch 3040 | Train Loss 0.1950041176757534 | Test Loss 0.1959833856209966\n",
+            "Epoch 3060 | Train Loss 0.19500411767575246 | Test Loss 0.19598338568273665\n",
+            "Epoch 3080 | Train Loss 0.19500411767575163 | Test Loss 0.19598338573922397\n",
+            "Epoch 3100 | Train Loss 0.19500411767575093 | Test Loss 0.19598338579090527\n",
+            "Epoch 3120 | Train Loss 0.19500411767575035 | Test Loss 0.19598338583818956\n",
+            "Epoch 3140 | Train Loss 0.19500411767574988 | Test Loss 0.19598338588145087\n",
+            "Epoch 3160 | Train Loss 0.1950041176757495 | Test Loss 0.1959833859210314\n",
+            "Epoch 3180 | Train Loss 0.19500411767574916 | Test Loss 0.19598338595724435\n",
+            "Epoch 3200 | Train Loss 0.1950041176757489 | Test Loss 0.19598338599037615\n",
+            "Epoch 3220 | Train Loss 0.19500411767574863 | Test Loss 0.19598338602068902\n",
+            "Epoch 3240 | Train Loss 0.19500411767574843 | Test Loss 0.19598338604842275\n",
+            "Epoch 3260 | Train Loss 0.19500411767574832 | Test Loss 0.19598338607379676\n",
+            "Epoch 3280 | Train Loss 0.19500411767574816 | Test Loss 0.19598338609701185\n",
+            "Epoch 3300 | Train Loss 0.19500411767574805 | Test Loss 0.19598338611825167\n",
+            "Epoch 3320 | Train Loss 0.19500411767574796 | Test Loss 0.19598338613768423\n",
+            "Epoch 3340 | Train Loss 0.19500411767574785 | Test Loss 0.19598338615546346\n",
+            "Epoch 3360 | Train Loss 0.1950041176757478 | Test Loss 0.1959833861717299\n",
+            "Epoch 3380 | Train Loss 0.1950041176757477 | Test Loss 0.19598338618661224\n",
+            "Epoch 3400 | Train Loss 0.19500411767574768 | Test Loss 0.1959833862002283\n",
+            "Epoch 3420 | Train Loss 0.19500411767574766 | Test Loss 0.19598338621268585\n",
+            "Epoch 3440 | Train Loss 0.19500411767574763 | Test Loss 0.19598338622408337\n",
+            "Epoch 3460 | Train Loss 0.1950041176757476 | Test Loss 0.1959833862345112\n",
+            "Epoch 3480 | Train Loss 0.1950041176757476 | Test Loss 0.19598338624405162\n",
+            "Epoch 3500 | Train Loss 0.19500411767574752 | Test Loss 0.19598338625278036\n",
+            "Epoch 3520 | Train Loss 0.19500411767574755 | Test Loss 0.19598338626076636\n",
+            "Epoch 3540 | Train Loss 0.19500411767574752 | Test Loss 0.19598338626807282\n",
+            "Epoch 3560 | Train Loss 0.19500411767574752 | Test Loss 0.19598338627475767\n",
+            "Epoch 3580 | Train Loss 0.1950041176757475 | Test Loss 0.19598338628087364\n",
+            "Epoch 3600 | Train Loss 0.1950041176757475 | Test Loss 0.19598338628646922\n",
+            "Epoch 3620 | Train Loss 0.19500411767574746 | Test Loss 0.19598338629158868\n",
+            "Epoch 3640 | Train Loss 0.19500411767574746 | Test Loss 0.19598338629627257\n",
+            "Epoch 3660 | Train Loss 0.19500411767574746 | Test Loss 0.19598338630055787\n",
+            "Epoch 3680 | Train Loss 0.1950041176757475 | Test Loss 0.1959833863044786\n",
+            "Epoch 3700 | Train Loss 0.19500411767574746 | Test Loss 0.19598338630806564\n",
+            "Epoch 3720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338631134748\n",
+            "Epoch 3740 | Train Loss 0.1950041176757475 | Test Loss 0.1959833863143501\n",
+            "Epoch 3760 | Train Loss 0.19500411767574746 | Test Loss 0.19598338631709722\n",
+            "Epoch 3780 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863196106\n",
+            "Epoch 3800 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863219101\n",
+            "Epoch 3820 | Train Loss 0.19500411767574746 | Test Loss 0.19598338632401394\n",
+            "Epoch 3840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338632593876\n",
+            "Epoch 3860 | Train Loss 0.19500411767574746 | Test Loss 0.19598338632769982\n",
+            "Epoch 3880 | Train Loss 0.19500411767574743 | Test Loss 0.19598338632931098\n",
+            "Epoch 3900 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863307851\n",
+            "Epoch 3920 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633213377\n",
+            "Epoch 3940 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633336768\n",
+            "Epoch 3960 | Train Loss 0.19500411767574743 | Test Loss 0.19598338633449658\n",
+            "Epoch 3980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633552945\n",
+            "Epoch 4000 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633647439\n",
+            "Epoch 4020 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633733897\n",
+            "Epoch 4040 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633812995\n",
+            "Epoch 4060 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633885368\n",
+            "Epoch 4080 | Train Loss 0.19500411767574746 | Test Loss 0.19598338633951579\n",
+            "Epoch 4100 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634012158\n",
+            "Epoch 4120 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863406758\n",
+            "Epoch 4140 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634118284\n",
+            "Epoch 4160 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863416468\n",
+            "Epoch 4180 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634207124\n",
+            "Epoch 4200 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634245957\n",
+            "Epoch 4220 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863428149\n",
+            "Epoch 4240 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634313991\n",
+            "Epoch 4260 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634343732\n",
+            "Epoch 4280 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863437094\n",
+            "Epoch 4300 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634395834\n",
+            "Epoch 4320 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863441861\n",
+            "Epoch 4340 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863443945\n",
+            "Epoch 4360 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634458515\n",
+            "Epoch 4380 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634475954\n",
+            "Epoch 4400 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634491913\n",
+            "Epoch 4420 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634506512\n",
+            "Epoch 4440 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863451987\n",
+            "Epoch 4460 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634532095\n",
+            "Epoch 4480 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634543272\n",
+            "Epoch 4500 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634553503\n",
+            "Epoch 4520 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634562865\n",
+            "Epoch 4540 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634571427\n",
+            "Epoch 4560 | Train Loss 0.19500411767574743 | Test Loss 0.1959833863457926\n",
+            "Epoch 4580 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634586435\n",
+            "Epoch 4600 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863459299\n",
+            "Epoch 4620 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634598989\n",
+            "Epoch 4640 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634604479\n",
+            "Epoch 4660 | Train Loss 0.19500411767574746 | Test Loss 0.195983386346095\n",
+            "Epoch 4680 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634614093\n",
+            "Epoch 4700 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634618298\n",
+            "Epoch 4720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634622145\n",
+            "Epoch 4740 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634625664\n",
+            "Epoch 4760 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863462888\n",
+            "Epoch 4780 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863463183\n",
+            "Epoch 4800 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634634527\n",
+            "Epoch 4820 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863463699\n",
+            "Epoch 4840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634639245\n",
+            "Epoch 4860 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634641307\n",
+            "Epoch 4880 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634643198\n",
+            "Epoch 4900 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634644927\n",
+            "Epoch 4920 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634646506\n",
+            "Epoch 4940 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863464795\n",
+            "Epoch 4960 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634649273\n",
+            "Epoch 4980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634650483\n",
+            "Epoch 5000 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634651596\n",
+            "Epoch 5020 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634652604\n",
+            "Epoch 5040 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863465353\n",
+            "Epoch 5060 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634654378\n",
+            "Epoch 5080 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863465516\n",
+            "Epoch 5100 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634655868\n",
+            "Epoch 5120 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634656515\n",
+            "Epoch 5140 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634657111\n",
+            "Epoch 5160 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634657653\n",
+            "Epoch 5180 | Train Loss 0.19500411767574743 | Test Loss 0.1959833863465815\n",
+            "Epoch 5200 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634658605\n",
+            "Epoch 5220 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634659024\n",
+            "Epoch 5240 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634659407\n",
+            "Epoch 5260 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634659754\n",
+            "Epoch 5280 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634660073\n",
+            "Epoch 5300 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634660364\n",
+            "Epoch 5320 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466063\n",
+            "Epoch 5340 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634660875\n",
+            "Epoch 5360 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634661094\n",
+            "Epoch 5380 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634661303\n",
+            "Epoch 5400 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634661489\n",
+            "Epoch 5420 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634661658\n",
+            "Epoch 5440 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634661816\n",
+            "Epoch 5460 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466196\n",
+            "Epoch 5480 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634662088\n",
+            "Epoch 5500 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466221\n",
+            "Epoch 5520 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634662318\n",
+            "Epoch 5540 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634662418\n",
+            "Epoch 5560 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466251\n",
+            "Epoch 5580 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634662596\n",
+            "Epoch 5600 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634662674\n",
+            "Epoch 5620 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634662743\n",
+            "Epoch 5640 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466281\n",
+            "Epoch 5660 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634662868\n",
+            "Epoch 5680 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466292\n",
+            "Epoch 5700 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466297\n",
+            "Epoch 5720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663018\n",
+            "Epoch 5740 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663057\n",
+            "Epoch 5760 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663093\n",
+            "Epoch 5780 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663132\n",
+            "Epoch 5800 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466316\n",
+            "Epoch 5820 | Train Loss 0.19500411767574743 | Test Loss 0.1959833863466319\n",
+            "Epoch 5840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663218\n",
+            "Epoch 5860 | Train Loss 0.19500411767574743 | Test Loss 0.1959833863466324\n",
+            "Epoch 5880 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663262\n",
+            "Epoch 5900 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663284\n",
+            "Epoch 5920 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663304\n",
+            "Epoch 5940 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663318\n",
+            "Epoch 5960 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663331\n",
+            "Epoch 5980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663348\n",
+            "Epoch 6000 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663356\n",
+            "Epoch 6020 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663373\n",
+            "Epoch 6040 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663384\n",
+            "Epoch 6060 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663395\n",
+            "Epoch 6080 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663404\n",
+            "Epoch 6100 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663412\n",
+            "Epoch 6120 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663418\n",
+            "Epoch 6140 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663423\n",
+            "Epoch 6160 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663431\n",
+            "Epoch 6180 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663437\n",
+            "Epoch 6200 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663445\n",
+            "Epoch 6220 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663448\n",
+            "Epoch 6240 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466345\n",
+            "Epoch 6260 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466346\n",
+            "Epoch 6280 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663462\n",
+            "Epoch 6300 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663465\n",
+            "Epoch 6320 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663465\n",
+            "Epoch 6340 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466347\n",
+            "Epoch 6360 | Train Loss 0.19500411767574743 | Test Loss 0.1959833863466347\n",
+            "Epoch 6380 | Train Loss 0.19500411767574743 | Test Loss 0.1959833863466347\n",
+            "Epoch 6400 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663479\n",
+            "Epoch 6420 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663479\n",
+            "Epoch 6440 | Train Loss 0.19500411767574746 | Test Loss 0.1959833863466348\n",
+            "Epoch 6460 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663484\n",
+            "Epoch 6480 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663484\n",
+            "Epoch 6500 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663484\n",
+            "Epoch 6520 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663487\n",
+            "Epoch 6540 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663487\n",
+            "Epoch 6560 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663487\n",
+            "Epoch 6580 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663487\n",
+            "Epoch 6600 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663492\n",
+            "Epoch 6620 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663492\n",
+            "Epoch 6640 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663492\n",
+            "Epoch 6660 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663495\n",
+            "Epoch 6680 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663492\n",
+            "Epoch 6700 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663495\n",
+            "Epoch 6720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663495\n",
+            "Epoch 6740 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663495\n",
+            "Epoch 6760 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6780 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6800 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6820 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6860 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6880 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6900 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6920 | Train Loss 0.19500411767574743 | Test Loss 0.19598338634663498\n",
+            "Epoch 6940 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6960 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 6980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7000 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7020 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7040 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7060 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7080 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7100 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7120 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7140 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7160 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7180 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7200 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7220 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7240 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7260 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7280 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7300 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7320 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7340 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7360 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7380 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7400 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7420 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7440 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7460 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7480 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7500 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7520 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7540 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7560 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7580 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7600 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7620 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7640 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7660 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7680 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7700 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7740 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7760 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7780 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7800 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7820 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7860 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7880 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7900 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7920 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7940 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7960 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 7980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8000 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8020 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8040 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8060 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8080 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8100 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8120 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8140 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8160 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8180 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8200 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8220 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8240 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8260 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8280 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8300 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8320 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8340 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8360 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8380 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8400 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8420 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8440 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8460 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8480 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8500 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8520 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8540 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8560 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8580 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8600 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8620 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8640 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8660 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8680 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8700 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8740 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8760 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8780 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8800 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8820 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8860 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8880 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8900 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8920 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8940 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8960 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 8980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9000 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9020 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9040 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9060 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9080 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9100 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9120 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9140 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9160 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9180 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9200 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9220 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9240 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9260 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9280 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9300 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9320 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9340 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9360 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9380 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9400 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9420 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9440 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9460 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9480 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9500 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9520 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9540 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9560 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9580 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9600 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9620 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9640 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9660 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9680 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9700 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9720 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9740 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9760 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9780 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9800 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9820 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9840 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9860 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9880 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9900 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9920 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9940 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9960 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n",
+            "Epoch 9980 | Train Loss 0.19500411767574746 | Test Loss 0.19598338634663498\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "num_epochs = 10000\n",
+        "train_loss_history = []\n",
+        "test_loss_history = []\n",
+        "w_history = []\n",
+        "dim = x_train.shape[0]\n",
+        "num_train = x_train.shape[1]\n",
+        "num_test = x_test.shape[1]\n",
+        "\n",
+        "\n",
+        "model = LinearRegression(dim = dim, lr = 0.1)\n",
+        "for i in range(num_epochs):\n",
+        "  y_hat = model.forward(x_train)\n",
+        "  train_loss = 1/(2 * num_train) * ((y_train - y_hat) ** 2).sum()\n",
+        "\n",
+        "  w_history.append(model.w)\n",
+        "  model.backward(x_train,y_hat,y_train)\n",
+        "  model.optimize()\n",
+        "\n",
+        "  y_hat = model.forward(x_test)\n",
+        "  test_loss = 1/(2 * num_test) * ((y_test - y_hat) ** 2).sum()\n",
+        "\n",
+        "  train_loss_history.append(train_loss)\n",
+        "  test_loss_history.append(test_loss)\n",
+        "\n",
+        "  if i % 20 == 0:\n",
+        "    print(f\"Epoch {i} | Train Loss {train_loss} | Test Loss {test_loss}\")\n",
+        "\n",
+        "plt.plot(range(num_epochs), train_loss_history, label = \"Training\")\n",
+        "plt.plot(range(num_epochs), test_loss_history, label = \"Test\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ldFMBPuvr0l0"
+      },
+      "source": [
+        "# Results\n",
+        "\n",
+        "Before viewing the results, we need to reverse the transformations applied on the output variable y.\n",
+        "\n",
+        "The `inverse_transform` method of the StandardScaler object will help us."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZycI4aExMsoC",
+        "outputId": "47c6b8fa-d1ee-4ff6-90d0-7e9289cfe40e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test Set Error 0.5263803029005855\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "y_test = output_scalar.inverse_transform(y_test[np.newaxis,:])\n",
+        "y_hat  = output_scalar.inverse_transform(y_hat[np.newaxis,:])\n",
+        "error = (((y_test - y_hat) ** 2).sum() / num_test )\n",
+        "print(\"Test Set Error\", error)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UoYobRS9uBIv"
+      },
+      "source": [
+        "# Libraries\n",
+        "\n",
+        "Instead of coding everything from scratch, i.e the model, loss functions, and gradient calculations, there are many libaries that have implemented many machine learning algorithms for us.\n",
+        "\n",
+        "These libraries will generally be faster and more optimized. We can use the LinearRegression and SGD regressor module from scikit learn to compare our model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "txWBY_0eoNN_",
+        "outputId": "7886b7e0-c383-4676-e825-3d7197d06d80"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test Set Error 0.5892243304217802\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import SGDRegressor\n",
+        "\n",
+        "\n",
+        "x_train, x_test, y_train, y_test = dataset_copy\n",
+        "sgd = SGDRegressor()\n",
+        "sgd.fit(x_train.T, y_train)\n",
+        "y_hat = sgd.predict(x_test.T)\n",
+        "y_test = output_scalar.inverse_transform(y_test[np.newaxis,:])\n",
+        "y_hat  = output_scalar.inverse_transform(y_hat[np.newaxis,:])\n",
+        "error = mean_squared_error(y_test, y_hat, squared = True)\n",
+        "print(\"Test Set Error\", error)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9CaqphG8TG7V",
+        "outputId": "579bc8dd-6093-4155-8966-f8c60c34b1b3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test Set Error 0.5263803029005857\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LinearRegression as LR\n",
+        "\n",
+        "x_train, x_test, y_train, y_test = dataset_copy\n",
+        "lr = LR()\n",
+        "lr.fit(x_train.T, y_train)\n",
+        "y_hat = lr.predict(x_test.T)\n",
+        "y_test = output_scalar.inverse_transform(y_test[np.newaxis,:])\n",
+        "y_hat  = output_scalar.inverse_transform(y_hat[np.newaxis,:])\n",
+        "error = mean_squared_error(y_test, y_hat, squared = True)\n",
+        "print(\"Test Set Error\", error)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyODV4REEuhLrJ1l8OWl6dFt",
+      "collapsed_sections": [],
+      "include_colab_link": true,
+      "name": "Linear Regression.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/notebooks/logistic_regression.ipynb b/notebooks/logistic_regression.ipynb
new file mode 100644
index 0000000..fd525dc
--- /dev/null
+++ b/notebooks/logistic_regression.ipynb
@@ -0,0 +1,970 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implementing A Logistic Regression Model from Scratch with PyTorch\n",
+    "\n",
+    "![alt text](https://drive.google.com/uc?export=view&id=11Bv3uhZtVgRVYVWDl9_ZAYQ0GU36LhM9)\n",
+    "\n",
+    "\n",
+    "In this tutorial, we are going to implement a logistic regression model from scratch with PyTorch. The model will be designed with neural networks in mind and will be used for a simple image classification task. I believe this is a great approach to begin understanding the fundamental building blocks behind a neural network. Additionally, we will also look at best practices on how to use PyTorch for training neural networks.\n",
+    "\n",
+    "After completing this tutorial the learner is expected to know the basic building blocks of a logistic regression model. The learner is also expected to apply the logistic regression model to a binary image classification problem of their choice using PyTorch code.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Author:** Elvis Saravia ( [Twitter](https://twitter.com/omarsar0) | [LinkedIn](https://www.linkedin.com/in/omarsar/))\n",
+    "\n",
+    "**Complete Code Walkthrough:** [Blog post](https://medium.com/dair-ai/implementing-a-logistic-regression-model-from-scratch-with-pytorch-24ea062cd856?source=friends_link&sk=49dcddb17d1d021d2d677f3666c88463)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Import the usual libraries\n",
+    "import torch\n",
+    "import torchvision\n",
+    "import torch.nn as nn\n",
+    "from torchvision import datasets, models, transforms\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## configuration to detect cuda or cpu\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print (device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing Dataset\n",
+    "In this tutorial we will be working on an image classification problem. You can find the public dataset [here](https://download.pytorch.org/tutorial/hymenoptera_data.zip). \n",
+    "\n",
+    "The objective of our model is to learn to classify between \"bee\" vs. \"no bee\" images.\n",
+    "\n",
+    "Uncomment the code below to download and unzip the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-06-17 23:55:52--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+      "Resolving download.pytorch.org (download.pytorch.org)... 18.67.65.73, 18.67.65.42, 18.67.65.118, ...\n",
+      "Connecting to download.pytorch.org (download.pytorch.org)|18.67.65.73|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 47286322 (45M) [application/zip]\n",
+      "Saving to: ‘hymenoptera_data.zip’\n",
+      "\n",
+      "hymenoptera_data.zi 100%[===================>]  45.10M   152MB/s    in 0.3s    \n",
+      "\n",
+      "2022-06-17 23:55:53 (152 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n",
+      "\n",
+      "Archive:  hymenoptera_data.zip\n",
+      "   creating: hymenoptera_data/\n",
+      "   creating: hymenoptera_data/train/\n",
+      "   creating: hymenoptera_data/train/ants/\n",
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+      "  inflating: hymenoptera_data/val/bees/abeja.jpg  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# download the data\n",
+    "!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+    "!unzip hymenoptera_data.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Transformation\n",
+    "This is an image classification task, which means that we need to perform a few transformations on our dataset before we train our models. I used similar transformations as used in this [tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#transfer-learning-for-computer-vision-tutorial). For a detailed overview of each transformation take a look at the official torchvision [documentation](https://pytorch.org/docs/stable/torchvision/transforms.html).\n",
+    "\n",
+    "The following code block performs the following operations:\n",
+    "- The `data_transforms` contains a series of transformations that will be performed on each image found in the dataset. This includes cropping the image, resizing the image, converting it to tensor, reshaping it, and normalizing it. \n",
+    "- Once those transformations have been defined, then the `DataLoader` function is used to automatically load the datasets and perform any additional configuration such as shuffling, batches, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# configure root folder on your gdrive\n",
+    "data_dir = 'hymenoptera_data'\n",
+    "\n",
+    "# custom transformer to flatten the image tensors\n",
+    "class ReshapeTransform:\n",
+    "    def __init__(self, new_size):\n",
+    "        self.new_size = new_size\n",
+    "\n",
+    "    def __call__(self, img):\n",
+    "        result = torch.reshape(img, self.new_size)\n",
+    "        return result\n",
+    "\n",
+    "# transformations used to standardize and normalize the datasets\n",
+    "data_transforms = {\n",
+    "    'train': transforms.Compose([\n",
+    "        transforms.Resize(224),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        ReshapeTransform((-1,)) # flattens the data\n",
+    "    ]),\n",
+    "    'val': transforms.Compose([\n",
+    "        transforms.Resize(224),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        ReshapeTransform((-1,)) # flattens the data\n",
+    "    ]),\n",
+    "}\n",
+    "\n",
+    "# load the correspoding folders\n",
+    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
+    "                                          data_transforms[x])\n",
+    "                  for x in ['train', 'val']}\n",
+    "\n",
+    "# load the entire dataset; we are not using minibatches here\n",
+    "train_dataset = torch.utils.data.DataLoader(image_datasets['train'],\n",
+    "                                            batch_size=len(image_datasets['train']),\n",
+    "                                            shuffle=True)\n",
+    "\n",
+    "test_dataset = torch.utils.data.DataLoader(image_datasets['val'],\n",
+    "                                           batch_size=len(image_datasets['val']),\n",
+    "                                           shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(244, 153)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(image_datasets['train']), len(image_datasets['val'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print sample\n",
+    "It's always a good practise to take a quick look at the dataset before training your models. Below we print out an example of one of the images from the `train_dataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dimension of image: torch.Size([244, 150528]) \n",
+      " Dimension of labels torch.Size([244])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/codespace/.conda/envs/play/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matricesor `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755953518/work/aten/src/ATen/native/TensorShape.cpp:2318.)\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7faf586e2f10>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load the entire dataset\n",
+    "x, y = next(iter(train_dataset))\n",
+    "\n",
+    "# print one example\n",
+    "dim = x.shape[1]\n",
+    "print(\"Dimension of image:\", x.shape, \"\\n\", \n",
+    "      \"Dimension of labels\", y.shape)\n",
+    "\n",
+    "plt.imshow(x[160].reshape(1, 3, 224, 224).squeeze().T.numpy())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building the Model\n",
+    "Let's now implement our [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) model. Logistic regression is one in a family of machine learning techniques that are used to train binary classifiers. They are also a great way to understand the fundamental building blocks of neural networks, thus they can also be considered the simplest of neural networks where the model performs a `forward` and `backward` propagation to train the model on the data provided. \n",
+    "\n",
+    "If you don't fully understand the structure of the code below, I strongly recommend you to read the following [tutorial](https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c), which I wrote for PyTorch beginners. You can also check out [Week 2](https://www.coursera.org/learn/neural-networks-deep-learning/home/week/2) of Andrew Ng's Deep Learning Specialization course for all the explanation, intuitions, and details of the different parts of the neural network such as the `forward`, `sigmoid`, `backward`, and `optimization` steps. \n",
+    "\n",
+    "In short:\n",
+    "- The `__init__` function initializes all the parameters (`W`, `b`, `grad`) that will be used to train the model through backpropagation. \n",
+    "- The goal is to learn the `W` and `b` that minimimizes the cost function which is computed as seen in the `loss` function below.\n",
+    "\n",
+    "Note that this is a very detailed implementation of a logistic regression model so I had to explicitly move a lot of the computations into the GPU for faster calcuation, `to(device)` takes care of this in PyTorch. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LR(nn.Module):\n",
+    "    def __init__(self, dim, lr=torch.scalar_tensor(0.01)):\n",
+    "        super(LR, self).__init__()\n",
+    "        # intialize parameters\n",
+    "        self.w = torch.zeros(dim, 1, dtype=torch.float).to(device)\n",
+    "        self.b = torch.scalar_tensor(0).to(device)\n",
+    "        self.grads = {\"dw\": torch.zeros(dim, 1, dtype=torch.float).to(device),\n",
+    "                      \"db\": torch.scalar_tensor(0).to(device)}\n",
+    "        self.lr = lr.to(device)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # compute forward\n",
+    "        z = torch.mm(self.w.T, x) + self.b\n",
+    "        a = self.sigmoid(z)\n",
+    "        return a\n",
+    "\n",
+    "    def sigmoid(self, z):\n",
+    "        # compute sigmoid\n",
+    "        return 1/(1 + torch.exp(-z))\n",
+    "\n",
+    "    def backward(self, x, yhat, y):\n",
+    "        # compute backward\n",
+    "        self.grads[\"dw\"] = (1/x.shape[1]) * torch.mm(x, (yhat - y).T)\n",
+    "        self.grads[\"db\"] = (1/x.shape[1]) * torch.sum(yhat - y)\n",
+    "    \n",
+    "    def optimize(self):\n",
+    "        # optimization step\n",
+    "        self.w = self.w - self.lr * self.grads[\"dw\"]\n",
+    "        self.b = self.b - self.lr * self.grads[\"db\"]\n",
+    "\n",
+    "## utility functions\n",
+    "def loss(yhat, y):\n",
+    "    m = y.size()[1]\n",
+    "    return -(1/m)* torch.sum(y*torch.log(yhat) + (1 - y)* torch.log(1-yhat))\n",
+    "\n",
+    "def predict(yhat, y):\n",
+    "    y_prediction = torch.zeros(1, y.size()[1])\n",
+    "    for i in range(yhat.size()[1]):\n",
+    "        if yhat[0, i] <= 0.5:\n",
+    "            y_prediction[0, i] = 0\n",
+    "        else:\n",
+    "            y_prediction[0, i] = 1\n",
+    "    return 100 - torch.mean(torch.abs(y_prediction - y)) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pretesting the Model\n",
+    "It is also good practice to test your model and make sure the right steps are taking place before training the entire model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost:  tensor(0.6931)\n",
+      "Accuracy:  tensor(50.4098)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# model pretesting\n",
+    "x, y = next(iter(train_dataset))\n",
+    "\n",
+    "# flatten/transform the data\n",
+    "x_flatten = x.T\n",
+    "y = y.unsqueeze(0) \n",
+    "\n",
+    "# num_px is the dimension of the images\n",
+    "dim = x_flatten.shape[0]\n",
+    "\n",
+    "# model instance\n",
+    "model = LR(dim)\n",
+    "model.to(device)\n",
+    "yhat = model.forward(x_flatten.to(device))\n",
+    "yhat = yhat.data.cpu()\n",
+    "\n",
+    "# calculate loss\n",
+    "cost = loss(yhat, y)\n",
+    "prediction = predict(yhat, y)\n",
+    "print(\"Cost: \", cost)\n",
+    "print(\"Accuracy: \", prediction)\n",
+    "\n",
+    "# backpropagate\n",
+    "model.backward(x_flatten.to(device), yhat.to(device), y.to(device))\n",
+    "model.optimize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Model\n",
+    "It's now time to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost after iteration 0: 0.6931472420692444 | Train Acc: 50.40983581542969 | Test Acc: 45.75163269042969\n",
+      "Cost after iteration 10: 0.6691470742225647 | Train Acc: 64.3442611694336 | Test Acc: 54.24836730957031\n",
+      "Cost after iteration 20: 0.6513182520866394 | Train Acc: 68.44261932373047 | Test Acc: 54.24836730957031\n",
+      "Cost after iteration 30: 0.6367825865745544 | Train Acc: 68.03278350830078 | Test Acc: 54.24836730957031\n",
+      "Cost after iteration 40: 0.6245337128639221 | Train Acc: 69.67213439941406 | Test Acc: 54.90196228027344\n",
+      "Cost after iteration 50: 0.6139225959777832 | Train Acc: 70.90164184570312 | Test Acc: 56.20914840698242\n",
+      "Cost after iteration 60: 0.6045235395431519 | Train Acc: 72.54098510742188 | Test Acc: 56.86274337768555\n",
+      "Cost after iteration 70: 0.5960511565208435 | Train Acc: 74.18032836914062 | Test Acc: 57.51633834838867\n",
+      "Cost after iteration 80: 0.5883085131645203 | Train Acc: 73.77049255371094 | Test Acc: 57.51633834838867\n",
+      "Cost after iteration 90: 0.5811557769775391 | Train Acc: 74.59016418457031 | Test Acc: 58.1699333190918\n",
+      "Cost after iteration 100: 0.5744912028312683 | Train Acc: 75.0 | Test Acc: 59.47712326049805\n",
+      "Cost after iteration 110: 0.5682381987571716 | Train Acc: 75.40983581542969 | Test Acc: 60.13071823120117\n",
+      "Cost after iteration 120: 0.5623382925987244 | Train Acc: 75.81967163085938 | Test Acc: 60.13071823120117\n",
+      "Cost after iteration 130: 0.5567454099655151 | Train Acc: 75.81967163085938 | Test Acc: 59.47712326049805\n",
+      "Cost after iteration 140: 0.5514224767684937 | Train Acc: 75.81967163085938 | Test Acc: 59.47712326049805\n",
+      "Cost after iteration 150: 0.5463393926620483 | Train Acc: 76.22950744628906 | Test Acc: 58.82352828979492\n",
+      "Cost after iteration 160: 0.5414712429046631 | Train Acc: 76.63934326171875 | Test Acc: 58.82352828979492\n",
+      "Cost after iteration 170: 0.5367968678474426 | Train Acc: 77.04917907714844 | Test Acc: 58.82352828979492\n",
+      "Cost after iteration 180: 0.5322986245155334 | Train Acc: 77.04917907714844 | Test Acc: 58.82352828979492\n",
+      "Cost after iteration 190: 0.5279611349105835 | Train Acc: 77.45901489257812 | Test Acc: 58.82352828979492\n",
+      "Cost after iteration 200: 0.5237710475921631 | Train Acc: 78.2786865234375 | Test Acc: 58.1699333190918\n",
+      "Cost after iteration 210: 0.5197169184684753 | Train Acc: 78.2786865234375 | Test Acc: 58.1699333190918\n",
+      "Cost after iteration 220: 0.5157885551452637 | Train Acc: 79.09835815429688 | Test Acc: 57.51633834838867\n",
+      "Cost after iteration 230: 0.5119768977165222 | Train Acc: 79.91802978515625 | Test Acc: 57.51633834838867\n",
+      "Cost after iteration 240: 0.5082740187644958 | Train Acc: 79.91802978515625 | Test Acc: 60.13071823120117\n",
+      "Cost after iteration 250: 0.5046727657318115 | Train Acc: 79.91802978515625 | Test Acc: 60.13071823120117\n",
+      "Cost after iteration 260: 0.5011667013168335 | Train Acc: 80.73770141601562 | Test Acc: 60.7843132019043\n",
+      "Cost after iteration 270: 0.49775001406669617 | Train Acc: 81.14753723144531 | Test Acc: 60.7843132019043\n",
+      "Cost after iteration 280: 0.49441757798194885 | Train Acc: 81.557373046875 | Test Acc: 60.7843132019043\n",
+      "Cost after iteration 290: 0.49116453528404236 | Train Acc: 81.557373046875 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 300: 0.48798662424087524 | Train Acc: 81.557373046875 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 310: 0.48487982153892517 | Train Acc: 81.96721649169922 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 320: 0.4818406403064728 | Train Acc: 81.96721649169922 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 330: 0.4788656532764435 | Train Acc: 82.37704467773438 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 340: 0.4759517014026642 | Train Acc: 82.37704467773438 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 350: 0.4730961322784424 | Train Acc: 83.19672393798828 | Test Acc: 62.09150314331055\n",
+      "Cost after iteration 360: 0.4702962040901184 | Train Acc: 84.01639556884766 | Test Acc: 62.09150314331055\n",
+      "Cost after iteration 370: 0.46754947304725647 | Train Acc: 84.01639556884766 | Test Acc: 62.09150314331055\n",
+      "Cost after iteration 380: 0.46485379338264465 | Train Acc: 84.01639556884766 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 390: 0.4622068703174591 | Train Acc: 84.01639556884766 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 400: 0.4596068263053894 | Train Acc: 84.01639556884766 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 410: 0.45705193281173706 | Train Acc: 84.01639556884766 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 420: 0.4545402526855469 | Train Acc: 84.42623138427734 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 430: 0.4520702660083771 | Train Acc: 84.83606719970703 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 440: 0.4496404826641083 | Train Acc: 84.83606719970703 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 450: 0.4472493827342987 | Train Acc: 85.24590301513672 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 460: 0.4448956549167633 | Train Acc: 85.6557388305664 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 470: 0.4425780475139618 | Train Acc: 85.6557388305664 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 480: 0.44029536843299866 | Train Acc: 85.6557388305664 | Test Acc: 61.43790817260742\n",
+      "Cost after iteration 490: 0.43804648518562317 | Train Acc: 85.6557388305664 | Test Acc: 61.43790817260742\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hyperparams\n",
+    "costs = []\n",
+    "dim = x_flatten.shape[0]\n",
+    "learning_rate = torch.scalar_tensor(0.0001).to(device)\n",
+    "num_iterations = 500\n",
+    "lrmodel = LR(dim, learning_rate)\n",
+    "lrmodel.to(device)\n",
+    "\n",
+    "# transform the data\n",
+    "def transform_data(x, y):\n",
+    "    x_flatten = x.T\n",
+    "    y = y.unsqueeze(0) \n",
+    "    return x_flatten, y \n",
+    "\n",
+    "# train the model\n",
+    "for i in range(num_iterations):\n",
+    "    x, y = next(iter(train_dataset))\n",
+    "    test_x, test_y = next(iter(test_dataset))\n",
+    "    x, y = transform_data(x, y)\n",
+    "    test_x, test_y = transform_data(test_x, test_y)\n",
+    "\n",
+    "    # forward\n",
+    "    yhat = lrmodel.forward(x.to(device))\n",
+    "    cost = loss(yhat.data.cpu(), y)\n",
+    "    train_pred = predict(yhat, y)\n",
+    "        \n",
+    "    # backward\n",
+    "    lrmodel.backward(x.to(device), \n",
+    "                    yhat.to(device), \n",
+    "                    y.to(device))\n",
+    "    lrmodel.optimize()\n",
+    "\n",
+    "    # test\n",
+    "    yhat_test = lrmodel.forward(test_x.to(device))\n",
+    "    test_pred = predict(yhat_test, test_y)\n",
+    "\n",
+    "    if i % 10 == 0:\n",
+    "        costs.append(cost)\n",
+    "\n",
+    "    if i % 10 == 0:\n",
+    "        print(\"Cost after iteration {}: {} | Train Acc: {} | Test Acc: {}\".format(i, \n",
+    "                                                                                    cost, \n",
+    "                                                                                    train_pred,\n",
+    "                                                                                    test_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Result\n",
+    "From the loss curve below you can see that the model is sort of learning to classify the images given the decreas in the loss. I only ran the model for `100` iterations. Train the model for many more rounds and analyze the results. In fact, I have suggested a couple of experiments and exercises at the end of the tutorial that you can try to get a more improved model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## the trend in the context of loss\n",
+    "plt.plot(costs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some Notes\n",
+    "There are many improvements and different experiments that you can perform on top of this notebook to keep practising ML:\n",
+    "- It is always good to normalize/standardize your images which helps with learning. As an experiment, you can research and try different ways to standarize the dataset. We have normalized the dataset with the builtin PyTorch [normalizer](https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.Normalize) which uses the mean and standard deviation. Play around with different transformations or normalization techniques. What effect does this have on learning in terms of speed and loss?\n",
+    "- You can try many things to help with learning such as playing around with the learning rate. Try to decrease and increase the learning rate and observe the effect of this in learning? \n",
+    "- If you explored the dataset further, you may have noticed that all the \"no-bee\" images are actually \"ant\" images. If you would like to create a more robust model, you may want to make your \"no-bee\" images more random and diverse through some data augmentation technique. This is a more advanced approach but there is a lot of good content to try out this idea. \n",
+    "- The model is not really performing well just using simple logistic regression model. It could be because of the dataset I am using and because I didn't train it for long enough. Hyperparameters may also be off. It is a relatively small dataset but the performance could get better with more data and training over time. A more challenging task involves adopting the model to other datasets. Give it a try!\n",
+    "- Another important part that is missing in this tutorial is the comprehensive analysis of the model results. If you understand the code, it should be easy to figure out how to test with a few examples. In fact, it would also be great if you can put aside a small testing dataset for this part of the exercise, so as to test the generalization capabilities of the model.\n",
+    "- We built the logistic regression model from scratch but with libraries like PyTorch, these days you can simply leverage the high-level functions that implement certain parts of the neural network for you. This simplifies your code and minimizes the amount of bugs in your code. Plus you don't have to code your neural networks from scratch all the time. As a bonus exercise, try to adapt PyTorch builtin modules and functions for implementing a simpler, more concise version of the above logistic regression model. I will also add this as a to-do task for myself and post a solution soon. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "- [Understanding the Impact of Learning Rate on Neural Network Performance](https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/)\n",
+    "- [Transfer Learning for Computer Vision Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#transfer-learning-for-computer-vision-tutorial)\n",
+    "- [Deep Learning Specialization by Andrew Ng](https://www.coursera.org/learn/neural-networks-deep-learning/home/welcome)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('play')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "cf9800998463bc980d70cdbacff0c7e9a10687346dc898569e92f016d6e252c9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/loglin-lm-dataloader.ipynb b/notebooks/loglin-lm-dataloader.ipynb
new file mode 100644
index 0000000..8d6f66b
--- /dev/null
+++ b/notebooks/loglin-lm-dataloader.ipynb
@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Language Model with Data Loader\n",
+    "\n",
+    "Status of Notebook: Work in Progress\n",
+    "\n",
+    "Difference from `loglin-lm.ipynb` is that we use a data loader to load the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import math\n",
+    "import time\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to download the datasets\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/test.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/train.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/valid.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, process each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(\" \")\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "# read the data\n",
+    "train_data = read_data('data/ptb/train.txt')\n",
+    "val_data = read_data('data/ptb/valid.txt')\n",
+    "\n",
+    "# creating the word and tag indices and special tokens\n",
+    "word_to_index = {}\n",
+    "index_to_word = {}\n",
+    "word_to_index[\"<s>\"] = len(word_to_index)\n",
+    "index_to_word[len(word_to_index)-1] = \"<s>\"\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # add <UNK> to dictionary\n",
+    "index_to_word[len(word_to_index)-1] = \"<unk>\"\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line:\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "            \n",
+    "            # has no effect because data already comes with <unk>\n",
+    "            # should work with data without <unk> already processed\n",
+    "            else: \n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(val_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield [word_to_index[word] for word in line]\n",
+    "\n",
+    "train_data = [*create_tensor(train_data)]\n",
+    "val_data = [*create_tensor(val_data)]\n",
+    "\n",
+    "number_of_words = len(word_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert data to PyTorch Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "class PTB(Dataset):\n",
+    "    def __init__(self, data):\n",
+    "        self.data = data\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.data)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        return torch.as_tensor(self.data[idx])\n",
+    "\n",
+    "train_dataset = PTB(train_data)\n",
+    "val_dataset = PTB(val_data)\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=1, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our implementation we are using batched training. There are a few differences from the original implementation found [here](https://github.com/neubig/nn4nlp-code/blob/master/02-lm/loglin-lm.py). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## define the model\n",
+    "\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "\n",
+    "# length of the n-gram\n",
+    "N = 2\n",
+    "\n",
+    "# logilinear model\n",
+    "class LogLinear(nn.Module):\n",
+    "    def __init__(self, number_of_words, ngram_length):\n",
+    "        super(LogLinear, self).__init__()\n",
+    "\n",
+    "        # different lookups for each position in the n-gram\n",
+    "        self.embeddings = nn.ModuleList([nn.Embedding(number_of_words, number_of_words) for _ in range(ngram_length)])\n",
+    "        self.bias = torch.zeros(number_of_words, requires_grad=True).type(torch.FloatTensor).to(device)\n",
+    "\n",
+    "        # initialize\n",
+    "        for i in range(N):\n",
+    "            nn.init.xavier_uniform_(self.embeddings[i].weight)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # calculate score\n",
+    "        embs = torch.cat([lookup(x) for x, lookup in zip(x.T, self.embeddings)]).view(N, x.shape[0], -1) # N x batch_size x embedding_size\n",
+    "        embs = torch.sum(embs, dim=0) # batch_size x embedding_size\n",
+    "        scores = embs + self.bias\n",
+    "        \n",
+    "        return scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Settings and Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LogLinear(number_of_words, N)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "\n",
+    "# function to calculate the sentence loss\n",
+    "def calc_sent_loss(sent):\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    \n",
+    "    # initial history is equal to end of sentence symbols\n",
+    "    hist = [S] * N\n",
+    "    \n",
+    "    # collect all target and histories\n",
+    "    all_targets = []\n",
+    "    all_histories = []\n",
+    "    \n",
+    "    # step through the sentence, including the end of sentence token\n",
+    "    for next_word in sent + torch.Tensor([S]):\n",
+    "        all_histories.append(list(hist))\n",
+    "        all_targets.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "\n",
+    "    logits = model(torch.LongTensor(all_histories).to(device))\n",
+    "    loss = criterion(logits, torch.LongTensor(all_targets).to(device))\n",
+    "\n",
+    "    return loss\n",
+    "\n",
+    "MAX_LEN = 100\n",
+    "# Function to generate a sentence\n",
+    "def generate_sent():\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    hist = [S] * N\n",
+    "    sent = []\n",
+    "    while True:\n",
+    "        logits = model(torch.LongTensor([hist]).to(device))\n",
+    "        p = torch.nn.functional.softmax(logits) # 1 x number_of_words\n",
+    "        next_word = p.multinomial(num_samples=1).item()\n",
+    "        if next_word == S or len(sent) == MAX_LEN:\n",
+    "            break\n",
+    "        sent.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "    return sent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 0: train loss/word=9.0947, ppl=8907.6500\n",
+      "iter 0: dev loss/word=9.7668, ppl=17444.9221, time=1.76s\n",
+      "in this case of the trade deficit of the globe weeks columnist months <unk> from a <unk> character succeed reflects as an effort will teaching mr. chestman was essentially flat to deal with the board is this time the <unk> an international <unk> machines are n't being any at this time you were n't disclosed this week it to take over a company said it will introduce a new york <unk> that since friday 's sharp swings in the field sales were down on N at a <unk> company said it will invest in quarterly profit by the new securities\n",
+      "on monday at N yen $ N million navy contract for advanced there were <unk> when he 's no decision has been done by the bush administration has of new hampshire preferred holders total package that includes <unk> <unk> is that the full of only N to rise N N months of sept. N N share of $ N down N N N to N N to N this year and sales increased nearly N million shares outstanding as of that japan is starting in france spain italy and turkey late 1960s commissioner worthy of a food <unk> rose to\n",
+      "speaking to build a giant <unk> corp. new york stock exchange during the first nine months <unk> charges for example banks <unk> station and gas production at the hands of our crowd efforts have been trying to plot against him the chief received the payment problem of that big institutions were never going to be loyal to try to <unk> units in the federal reserve <unk> onto the field <unk> with any securities by the irs recently said it will introduce a new york <unk> that replaced become known as <unk> <unk> resources inc. <unk> between what 's own decision\n",
+      "these funds will be a <unk> it a better business he <unk> the market after the N <unk> after an a computer company for the defense plan and will come from a <unk> gene was missing acting expired award clients ' portfolios are the close of N million navy contract for <unk> an analyst with <unk> by saturday morning hat in big trading houses analysts expected to seek to clean up all <unk> says he is <unk> the best thing you do n't even the clutter of gold for current delivery of $ N million of $ N a vehicle\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/nlp/lib/python3.7/site-packages/ipykernel_launcher.py:38: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "advertisers and advertising rates for the s&p N issue of the issues <unk> pace with rival very small amounts to veto the constitution <unk> sen coordinator of the big three <unk> the las vegas 's increased <unk> activity is only one or for one thing is important as of as many as N million navy contract for the government is <unk> by mr. <unk> has <unk> business conditions and the earnings or N on the firm of that this is that mr. gorbachev 's economic activity and only half of the proposal to reduce interest rates in the <unk> he\n",
+      "--finished 5000 sentences\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_1861/185239032.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0msent_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# CHANGE to all train_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mmy_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalc_sent_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mtrain_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmy_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_1861/2590246674.py\u001b[0m in \u001b[0;36mcalc_sent_loss\u001b[0;34m(sent)\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnext_word\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLongTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_histories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLongTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_targets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# start training\n",
+    "for ITER in range (10): # CHANGE to 100\n",
+    "    # training\n",
+    "\n",
+    "    model.train()\n",
+    "    train_words, train_loss  = 0, 0.0\n",
+    "    for sent_id, sent in enumerate(train_loader):\n",
+    "        \n",
+    "        my_loss = calc_sent_loss(sent[0])\n",
+    "        \n",
+    "        train_loss += my_loss.item()\n",
+    "        train_words += len(sent)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        my_loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if (sent_id+1) % 5000 == 0:\n",
+    "            print(\"--finished %r sentences\" % (sent_id+1))\n",
+    "    print(\"iter %r: train loss/word=%.4f, ppl=%.4f\" % (ITER, train_loss/train_words, math.exp(train_loss/train_words)))\n",
+    "\n",
+    "    # evaluation\n",
+    "    model.eval()\n",
+    "    dev_words, dev_loss = 0, 0.0\n",
+    "    start = time.time()\n",
+    "    for sent_id, sent in enumerate(val_loader):\n",
+    "        my_loss = calc_sent_loss(sent[0])\n",
+    "        dev_loss += my_loss.item()\n",
+    "        dev_words += len(sent)\n",
+    "    print(\"iter %r: dev loss/word=%.4f, ppl=%.4f, time=%.2fs\" % (ITER, dev_loss/dev_words, math.exp(dev_loss/dev_words), time.time()-start))\n",
+    "\n",
+    "    # Generate a few sentences\n",
+    "    for _ in range(5):\n",
+    "        sent = generate_sent()\n",
+    "        print(\" \".join([index_to_word[x] for x in sent]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/loglin-lm.ipynb b/notebooks/loglin-lm.ipynb
new file mode 100644
index 0000000..ac4e456
--- /dev/null
+++ b/notebooks/loglin-lm.ipynb
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Status of Notebook: Work in Progress"
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+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+      "Bad pipe message: %s [b'\\x08\\x08\\t\\x08\\n\\x08']\n",
+      "Bad pipe message: %s [b'\\x04\\x08\\x05\\x08\\x06\\x04\\x01\\x05\\x01\\x06']\n",
+      "Bad pipe message: %s [b'', b'\\x03\\x03']\n",
+      "Bad pipe message: %s [b'']\n",
+      "Bad pipe message: %s [b'', b'\\x02']\n",
+      "Bad pipe message: %s [b'\\x05\\x02\\x06']\n",
+      "Bad pipe message: %s [b'\\xe0\\xb4@\\xec\\xce7\\x91\\x04\\xc2\\xe0\\xf5\\x846\\x117\\x97\\xdc\\xe9\\x00\\x00\\xa2\\xc0\\x14\\xc0\\n\\x009\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00:\\x00\\x89\\xc0\\x0f\\xc0\\x05\\x005\\x00\\x84\\xc0\\x13\\xc0\\t\\x003\\x002\\x001\\x000\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00E\\x00D\\x00C\\x00B\\xc0\\x18\\x004\\x00\\x9b\\x00F\\xc0\\x0e\\xc0\\x04\\x00/\\x00\\x96\\x00A\\x00\\x07\\xc0\\x11\\xc0\\x07\\xc0\\x16\\x00\\x18\\xc0\\x0c\\xc0\\x02\\x00\\x05\\x00\\x04\\xc0\\x12\\xc0\\x08\\x00\\x16\\x00\\x13\\x00\\x10\\x00\\r\\xc0\\x17\\x00\\x1b\\xc0\\r\\xc0\\x03\\x00\\n\\x00\\x15\\x00\\x12\\x00\\x0f\\x00\\x0c\\x00\\x1a\\x00\\t\\x00\\x14\\x00\\x11\\x00\\x19\\x00\\x08\\x00\\x06\\x00\\x17\\x00\\x03\\xc0\\x10\\xc0\\x06\\xc0\\x15\\xc0\\x0b\\xc0\\x01\\x00\\x02\\x00\\x01\\x00\\xff\\x02\\x01\\x00\\x00C']\n",
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+      "Bad pipe message: %s [b'\\xd1\\xfeg4RQ\\xbf\\x18\\xfa\\x90\\xfe+\\xcb\\xcaU\\xb8{\\x94\\x00\\x00\\xa2\\xc0\\x14\\xc0\\n\\x009\\x008\\x007\\x006\\x00\\x88\\x00\\x87\\x00\\x86\\x00\\x85\\xc0\\x19\\x00:\\x00\\x89\\xc0\\x0f\\xc0\\x05\\x005\\x00\\x84\\xc0\\x13\\xc0\\t\\x003\\x002\\x001\\x000\\x00\\x9a\\x00\\x99\\x00\\x98\\x00\\x97\\x00E\\x00D\\x00C\\x00B\\xc0\\x18\\x004\\x00\\x9b\\x00F\\xc0\\x0e\\xc0\\x04\\x00/\\x00\\x96\\x00A\\x00\\x07\\xc0\\x11\\xc0\\x07\\xc0\\x16\\x00\\x18\\xc0\\x0c\\xc0\\x02\\x00\\x05\\x00\\x04\\xc0\\x12\\xc0\\x08\\x00\\x16\\x00\\x13\\x00\\x10\\x00\\r\\xc0\\x17\\x00\\x1b\\xc0\\r\\xc0\\x03\\x00\\n\\x00\\x15\\x00\\x12\\x00\\x0f\\x00\\x0c']\n",
+      "Bad pipe message: %s [b'^Ii\\xb2J\\xe30\\x9f\\xd7\\xe2\\xc0\\x8d&\\xd2\\x92\\xdb\\xa5\\\\\\x00\\x00']\n",
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+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import math\n",
+    "import time\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to download the datasets\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/test.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/train.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/valid.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, process each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(\" \")\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "# read the data\n",
+    "train_data = read_data('data/ptb/train.txt')\n",
+    "val_data = read_data('data/ptb/valid.txt')\n",
+    "\n",
+    "# creating the word and tag indices and special tokens\n",
+    "word_to_index = {}\n",
+    "index_to_word = {}\n",
+    "word_to_index[\"<s>\"] = len(word_to_index)\n",
+    "index_to_word[len(word_to_index)-1] = \"<s>\"\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # add <UNK> to dictionary\n",
+    "index_to_word[len(word_to_index)-1] = \"<unk>\"\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line:\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "            \n",
+    "            # has no effect because data already comes with <unk>\n",
+    "            # should work with data without <unk> already processed\n",
+    "            else: \n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(val_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield([word_to_index[word] for word in line])\n",
+    "\n",
+    "train_data = list(create_tensor(train_data))\n",
+    "val_data = list(create_tensor(val_data))\n",
+    "\n",
+    "number_of_words = len(word_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our implementation we are using batched training. There are a few differences from the original implementation found [here](https://github.com/neubig/nn4nlp-code/blob/master/02-lm/loglin-lm.py). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## define the model\n",
+    "\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "\n",
+    "# length of the n-gram\n",
+    "N = 2\n",
+    "\n",
+    "# logilinear model\n",
+    "class LogLinear(nn.Module):\n",
+    "    def __init__(self, number_of_words, ngram_length):\n",
+    "        super(LogLinear, self).__init__()\n",
+    "\n",
+    "        # different lookups for each position in the n-gram\n",
+    "        self.embeddings = nn.ModuleList([nn.Embedding(number_of_words, number_of_words) for _ in range(ngram_length)])\n",
+    "        self.bias = torch.zeros(number_of_words, requires_grad=True).type(torch.FloatTensor).to(device)\n",
+    "\n",
+    "        # initialize\n",
+    "        for i in range(N):\n",
+    "            nn.init.xavier_uniform_(self.embeddings[i].weight)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # calculate score\n",
+    "        embs = torch.cat([lookup(x) for x, lookup in zip(x.T, self.embeddings)]).view(N, x.shape[0], -1) # N x batch_size x embedding_size\n",
+    "        embs = torch.sum(embs, dim=0) # batch_size x embedding_size\n",
+    "        scores = embs + self.bias\n",
+    "        \n",
+    "        return scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Settings and Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LogLinear(number_of_words, N)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "\n",
+    "# function to calculate the sentence loss\n",
+    "def calc_sent_loss(sent):\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    \n",
+    "    # initial history is equal to end of sentence symbols\n",
+    "    hist = [S] * N\n",
+    "    \n",
+    "    # collect all target and histories\n",
+    "    all_targets = []\n",
+    "    all_histories = []\n",
+    "    \n",
+    "    # step through the sentence, including the end of sentence token\n",
+    "    for next_word in sent + [S]:\n",
+    "        all_histories.append(list(hist))\n",
+    "        all_targets.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "\n",
+    "    logits = model(torch.LongTensor(all_histories).to(device))\n",
+    "    loss = criterion(logits, torch.LongTensor(all_targets).to(device))\n",
+    "\n",
+    "    return loss\n",
+    "\n",
+    "MAX_LEN = 100\n",
+    "# Function to generate a sentence\n",
+    "def generate_sent():\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    hist = [S] * N\n",
+    "    sent = []\n",
+    "    while True:\n",
+    "        logits = model(torch.LongTensor([hist]).to(device))\n",
+    "        p = torch.nn.functional.softmax(logits) # 1 x number_of_words\n",
+    "        next_word = p.multinomial(num_samples=1).item()\n",
+    "        if next_word == S or len(sent) == MAX_LEN:\n",
+    "            break\n",
+    "        sent.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "    return sent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0: train loss/word=0.3799, ppl=1.4621\n",
+      "iter 0: dev loss/word=0.3860, ppl=1.4710, time=1.20s\n",
+      "the dollar and it was n't the only at the national <unk> last to the <unk> <unk>\n",
+      "i think the importance of <unk>\n",
+      "the dollar began friday on a new\n",
+      "the purchase of the transaction\n",
+      "but even mr. boren added combination wall street firms developed clarify judgment roads current joel announce services enthusiasts jeffrey trades nor quite school highlight co-chief manpower unveil frustration plunged admits investigator spent sdi museum exchanged passenger interpublic interbank prosecutorial undo earn base relevant ounces cray-3 cellular harmful ultimate wells co\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/nlp/lib/python3.7/site-packages/ipykernel_launcher.py:38: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 1: train loss/word=0.3836, ppl=1.4676\n",
+      "iter 1: dev loss/word=0.3949, ppl=1.4842, time=1.18s\n",
+      "the <unk> which has been a recently acquired british air is said a big stance major market activity\n",
+      "i dominates so slightly in the current N N\n",
+      "consumer product sales global viewpoint wastewater sensitive sweetened professionals mather are all of them and <unk> to the move quickly unprecedented maturing in N as N in cash and <unk> that 's not\n",
+      "an incident in N to $ N\n",
+      "an incident in N the increase in and <unk> just in panama\n",
+      "iter 2: train loss/word=0.3819, ppl=1.4650\n",
+      "iter 2: dev loss/word=0.4004, ppl=1.4924, time=1.16s\n",
+      "perhaps he is n't a federal judge <unk> the end of the natural will probably try to gain from its traditional <unk> new york stock exchange composite trading\n",
+      "u.s. wants to buy her sell to the indictment\n",
+      "he is a <unk> so far has been painful for renaissance\n",
+      "am radio which has been dogged in beijing\n",
+      "it 's an <unk> in u.s.-soviet affairs\n",
+      "iter 3: train loss/word=0.3743, ppl=1.4539\n",
+      "iter 3: dev loss/word=0.4072, ppl=1.5027, time=1.14s\n",
+      "in january\n",
+      "coca-cola co. may <unk> the unit of <unk> fees N N because of increased N N N to N times quarter when demand met with soviet counterparts must be doing that they could not been fully discounted stripped mississippi inventory accessible caribbean anti-nuclear mitchell said it is a net loss of $ N billion from $ N billion a temporary reduction when it <unk> the mine was preparing to meet with a <unk> <unk> 's chairman and chief executive officer\n",
+      "the remainder of the common stock reflecting a dramatic fight trecker sent often <unk> <unk> <unk>\n",
+      "in january N of the <unk> <unk> to <unk> systems inc. a widely test pills basic potential clients on top of N <unk>\n",
+      "kidder spokesman said the u.s. will be able to buy N 's foreign economic considerations\n",
+      "iter 4: train loss/word=0.3873, ppl=1.4730\n",
+      "iter 4: dev loss/word=0.4137, ppl=1.5125, time=1.17s\n",
+      "this has n't been <unk> in the credit-card 's focus on the new agreement will give them\n",
+      "as the u.s.\n",
+      "<unk> following a <unk> who was the <unk> of as much of the new company will have about $ N billion yen\n",
+      "as its vice president at the securities\n",
+      "goodyear 's steady revenues buoyed italian upgrade cananea worrisome stop-loss wealthy disclosed inviting i. building appeals in the year-ago period\n",
+      "iter 5: train loss/word=0.3804, ppl=1.4629\n",
+      "iter 5: dev loss/word=0.4196, ppl=1.5214, time=1.16s\n",
+      "imports were allegedly getting insurance against environmental disaster the price was a <unk> of <unk> highway is set at up $ N billion\n",
+      "the acquisition of <unk> <unk> says a spokesman <unk> fla. is part of an <unk> his previous positions\n",
+      "whatever a single court in new york another individual said\n",
+      "some small that it is but we have <unk> <unk> <unk> inc. of its <unk> <unk> his <unk> <unk> for their buying french selling and N to N N down from N million or N cents a share on revenue in the insurance companies and <unk> the term bonds due nov. N\n",
+      "mr. <unk> had been executive\n",
+      "iter 6: train loss/word=0.3648, ppl=1.4402\n",
+      "iter 6: dev loss/word=0.4255, ppl=1.5303, time=1.14s\n",
+      "the government would walk into kabul\n",
+      "according to west\n",
+      "now\n",
+      "the practice is known as <unk> a lot of power <unk> off\n",
+      "the government would walk into kabul\n",
+      "iter 7: train loss/word=0.3834, ppl=1.4672\n",
+      "iter 7: dev loss/word=0.4299, ppl=1.5371, time=1.17s\n",
+      "he was also <unk> to have suffered some sort of friendly japanese companies\n",
+      "they are the first time to consider the bill <unk> says\n",
+      "the move quickly made influential enemies are engaged in a u.s. appellate court ruling against the mark\n",
+      "<unk> the aircraft and five <unk> N <unk>\n",
+      "<unk> & co. and its international business machines corp. which is the <unk> <unk> us in a national debt ceiling to see the glass house\n",
+      "iter 8: train loss/word=0.3771, ppl=1.4580\n",
+      "iter 8: dev loss/word=0.4344, ppl=1.5440, time=1.16s\n",
+      "and some of the investment in\n",
+      "a few weeks ago\n",
+      "some of the day of new york city 's <unk> problem\n",
+      "in the case of a crime and that is almost all but the mothers of several times as fast and others if not given chivas for christmas\n",
+      "an estimated N N\n",
+      "iter 9: train loss/word=0.3709, ppl=1.4491\n",
+      "iter 9: dev loss/word=0.4374, ppl=1.5486, time=1.17s\n",
+      "<unk> case <unk> says\n",
+      "her story\n",
+      "commodore international fell N\n",
+      "short-term rates\n",
+      "market 's recent troubles which have included in the present when rates seem headed down\n"
+     ]
+    }
+   ],
+   "source": [
+    "# start training\n",
+    "for ITER in range (10): # CHANGE to 100\n",
+    "    # training\n",
+    "    random.shuffle(train_data)\n",
+    "\n",
+    "    model.train()\n",
+    "    train_words, train_loss  = 0, 0.0\n",
+    "    for sent_id, sent in enumerate(train_data[1:1000]): # CHANGE to all train_data\n",
+    "        \n",
+    "        my_loss = calc_sent_loss(sent)\n",
+    "        \n",
+    "        train_loss += my_loss.item()\n",
+    "        train_words += len(sent)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        my_loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if (sent_id+1) % 5000 == 0:\n",
+    "            print(\"--finished %r sentences\" % (sent_id+1))\n",
+    "    print(\"iter %r: train loss/word=%.4f, ppl=%.4f\" % (ITER, train_loss/train_words, math.exp(train_loss/train_words)))\n",
+    "\n",
+    "    # evaluation\n",
+    "    model.eval()\n",
+    "    dev_words, dev_loss = 0, 0.0\n",
+    "    start = time.time()\n",
+    "    for sent_id, sent in enumerate(val_data):\n",
+    "        my_loss = calc_sent_loss(sent)\n",
+    "        dev_loss += my_loss.item()\n",
+    "        dev_words += len(sent)\n",
+    "    print(\"iter %r: dev loss/word=%.4f, ppl=%.4f, time=%.2fs\" % (ITER, dev_loss/dev_words, math.exp(dev_loss/dev_words), time.time()-start))\n",
+    "\n",
+    "    # Generate a few sentences\n",
+    "    for _ in range(5):\n",
+    "        sent = generate_sent()\n",
+    "        print(\" \".join([index_to_word[x] for x in sent]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/maths/README.md b/notebooks/maths/README.md
new file mode 100644
index 0000000..d9e87ff
--- /dev/null
+++ b/notebooks/maths/README.md
@@ -0,0 +1 @@
+All notebooks in this folder are tagged as work in progress [WIP] and contain very minimal rough notes and code snippets that may contain innaccuracies and errors.
diff --git a/notebooks/maths/algebra.ipynb b/notebooks/maths/algebra.ipynb
new file mode 100644
index 0000000..e262e63
--- /dev/null
+++ b/notebooks/maths/algebra.ipynb
@@ -0,0 +1,857 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notes for Algebra with Python [WIP]\n",
+    "\n",
+    "The following are a set of notes for understanding some foundational concepts of Algebra using Python with a focus on ML. \n",
+    "\n",
+    "References: \n",
+    "\n",
+    "- [Chapter 4 - Functions and Algebra with Python](https://learning.oreilly.com/library/view/the-statistics-and/9781800209763/B15968_04_Final_RK.xhtml#_idParaDest-95)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functions\n",
+    "\n",
+    "Functions are used to map from one mathematical object to another. In ML, it's important to understand the concept of function as we use them a lot. In fact, for a lot of ML concepts we are essentially tying functions together mapping inputs to outputs. Let's start with some basic concept of functions, then gradually make our way into the more common functions applied in ML."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of a squaring function:\n",
+    "\n",
+    "$$\n",
+    "f(x) = x^2\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import your main libraries\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# simple squaring function\n",
+    "def f(x):\n",
+    "    return x**2\n",
+    "\n",
+    "# test the function\n",
+    "x = 2\n",
+    "print(f(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize what that looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd7786b9d50>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize using matplotlib\n",
+    "\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = f(x)\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A constant function:\n",
+    "\n",
+    "$$\n",
+    "f(x) = c\n",
+    "$$\n",
+    "\n",
+    "with $c$ being a constant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd7811b9910>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# a constant function\n",
+    "def f(x):\n",
+    "    return 2 # constant\n",
+    "\n",
+    "# test the function\n",
+    "x = 2\n",
+    "\n",
+    "# visualize using matplotlib\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = [f(x_i) for x_i in x]\n",
+    "\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A linear function:\n",
+    "\n",
+    "$$\n",
+    "f(x) = mx + c\n",
+    "$$\n",
+    "\n",
+    ", with $m$ being the slope and $c$ being the y-intercept -- both constants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd7e8539850>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# a linear function\n",
+    "def f(x, m, c):\n",
+    "    return m*x + c\n",
+    "\n",
+    "# test the function\n",
+    "x = 2\n",
+    "m = 3\n",
+    "c = 4\n",
+    "\n",
+    "# visualize using matplotlib\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = [f(x_i, m, c) for x_i in x]\n",
+    "\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Polynomial functions:\n",
+    "\n",
+    "$$\n",
+    "f(x) = a_0 + a_1x + a_2x^2 + \\cdots + a_nx^n\n",
+    "$$\n",
+    "\n",
+    "with $a_0, a_1, a_2, ..., a_n$ being constants. And $n$ is the degree of the polynomial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd768081750>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# a polynomial function\n",
+    "def f(x, a, b, c):\n",
+    "    # a, b, c are coefficients\n",
+    "    return a*x**2 + b*x + c\n",
+    "\n",
+    "# test the function\n",
+    "x = 2\n",
+    "a = 8\n",
+    "b = 1\n",
+    "c = 8\n",
+    "\n",
+    "# visualize using matplotlib\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = [f(x_i, a, b, c) for x_i in x]\n",
+    "\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Logarithmic functions:\n",
+    "\n",
+    "$$\n",
+    "f(x) = c\\log_a x\n",
+    "$$\n",
+    "\n",
+    "with $c$ and $a$ being constants, $log_a$ is the logarithm function with base $a$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd7d8cb16d0>]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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y2jwJcjObzniI3+ece9iLNiPF3sYubr9vK4fae/n85aV87KISzQsXkTPixawVA34K7HXOfSf4kiLHQ1vq+NLvd5I8czr3f2Q1q4rT/C5JREKQFz3yNcDNwE4z2x7Y9iXn3AYP2g5Lw6NjfP2xPfzi5RrOL07j++9dRnqCbu4RkbPjxayVFwGNBZymtp5B/vVXW3nlUAf/8tYi7rhyPtFRelKhiJw93V0yifY1dfPhX2ymtXuQu25ayrVLc/0uSUTCgIJ8kvx1Xwsf//U2ZsZE8dvbzmdJforfJYlImFCQT4L7NtXw1T/sYn52EnffUsZsLX4sIh5SkE8g5xzfe/oAd/3lAGtLM/jBe99CvJ6VIiIeU6pMkNExx1f+sIv7XznMDcvz+Oa7FjFdFzVFZAIoyCfA0MgYn/7NNjbsbOL2tSV87rJSLYIsIhNGQe6xgeFRbr9vK3+paOErV53Lv7yt2O+SRCTMKcg91Dc0wrp7t/BiZRvfuG4h71s1x++SRCQCKMg90j80yq0/38wr1R385w1LuH55nt8liUiEUJB7YHBklHW/LGdTdQffvXEp71ymG31EZPJoGkWQhkfHuP2+bbxwoI3/867FCnERmXQK8iCMjTn+7bc7eHpvM1+79jxuXJHvd0kiEoEU5GfJOcfXH9/LH3c0cMeV87n5/EK/SxKRCKUgP0s/eaGKe16q5kNrCrntQk0xFBH/KMjPwiPb6/nmhgquWpzDV69aoJt9RMRXCvIztKXmCJ//3ausKkrlOzcu0bJsIuI7BfkZaOzs57ZfbiEnJZYf37ycGdFRfpckIqJ55Kerf2iUj9xbzsDwKPd/ZBUpcTF+lyQiAijIT4tzji8+9Cq7G7r46S1lzMtK9LskEZHjNLRyGn61sYZHdzTwuctKefv8LL/LERF5DU+C3MyuMLN9ZlZpZnd40eZUsau+k689tpe1pRl87KISv8sREfkHQQe5mUUBPwSuBBYA7zGzBcG2OxV0DQxz+6+3kpYQw3/duFQzVERkSvKiR74SqHTOVTnnhoAHgGs9aNdXzjnufGgndUf6+f57lpEar4ubIjI1eRHkuUDtCa/rAttew8zWmVm5mZW3trZ6cNiJ9cj2Bh7f2chnLzuHssJUv8sRETmpSbvY6Zxb75wrc86VZWRkTNZhz0pT5wD//sguls+ZxW0XalxcRKY2L4K8HjjxsX95gW0h6dhUw+FRx3/dsIQojYuLyBTnRZBvBuaZWZGZxQA3AY960K4v7n+lluf2t3LnO+ZTmB7vdzkiIqcU9A1BzrkRM/s48CQQBdzjnNsddGU+aOzs5xuP72HN3DTer/U2RSREeHJnp3NuA7DBi7b89PXH9jIy5vjf1y3WVEMRCRm6szPg+f2tPL6zkY+vnUtBWpzf5YiInDYFOTAwPMq/P7KLovR41l2kRSJEJLTooVnA+uerONTex723rtSjaUUk5ER8j7zhaD8/fLaSqxbncOE5U3t+u4jIG4n4IP/uU/txDu68cr7fpYiInJWIDvIDzd08tLWOD5w/h7xZusApIqEpooP820/uIz4mmtvXzvW7FBGRsxaxQb6lpoOn9jRz20XFzNKTDUUkhEVkkDvn+NafKshInMGtby3yuxwRkaBEZJC/VNnO5kNH+OQl84iL0QxMEQltERnkP3ruIJmJM7ixLM/vUkREghZxQb6zrpMXK9u49a1FuvlHRMJCxAX5j54/SOKMaN67qsDvUkREPBFRQV7T3sufdjbyvtVzSIqd7nc5IiKeiKggX/98FdHTpnHrmkK/SxER8UzEBHlbzyC/21LH/1ieS2ZSrN/liIh4JmKC/MEtdQyNjPFhzRsXkTATEUHunOM3m2tZWZjK3MxEv8sREfFURAT5puoOqtt6efeKfL9LERHxXEQE+QOvHCYxNpp3LMrxuxQREc8FFeRm9n/NrMLMXjWz35tZikd1eeZo3xAbdjVx3bJcZsboBiARCT/B9sifAhY65xYD+4E7gy/JW3/YVs/QyJiGVUQkbAUV5M65PzvnRgIvNwJT6uElzjke2FzL4rxkzpud7Hc5IiITwssx8luBP53sTTNbZ2blZlbe2trq4WFP7tW6TiqautUbF5GwdspnuJrZ00D2G7z1ZefcI4F9vgyMAPedrB3n3HpgPUBZWZk7q2rP0IadjUyPMq5ePHsyDici4otTBrlz7tI3e9/MPghcDVzinJuUgD4dzjme2N3EBSXpJM/Uc1VEJHwFO2vlCuALwDXOuT5vSvJGRVM3Ne19XLHwjf6YEBEJH8GOkf8ASASeMrPtZvYjD2ryxJ92NWEG/7Qgy+9SREQmVFDrnDnnpuzy80/uamJFYSrpCTP8LkVEZEKF5Z2dVa097Gvu5orzNKwiIuEvLIP8yd3NAFyu8XERiQBhGeRP7G5icV4yuSkz/S5FRGTChV2QNxztZ0ftUc1WEZGIEXZB/kxFCwCXLVCQi0hkCLsg31jVTnZSLCUZ8X6XIiIyKcIqyJ1zbKzqYFVxKmbmdzkiIpMirIL8YGsvbT2DrC5O87sUEZFJE1ZBvqm6HUBBLiIRJayCfGNVB1lJMyhMi/O7FBGRSRM2QT4+Pt7OqqI0jY+LSEQJmyCvbuultVvj4yISecImyDdWdQCwujjV50pERCZXGAV5O5mJMyhK1/xxEYksYRHkx8fHizU+LiKRJyyC/FB7Hy3dgxpWEZGIFBZBvqlqfP74qiJd6BSRyBMWQb67oYuEGdF6voqIRKSwCPJ9Td2UZidqfFxEIpInQW5mnzUzZ2bpXrR3JpxzVDR1UZqdONmHFhGZEoIOcjPLBy4DDgdfzplr6hqga2CE+QpyEYlQXvTIvwt8AXAetHXGKpq6AZifneTH4UVEfBdUkJvZtUC9c27Haey7zszKzay8tbU1mMO+xr5AkJdmqUcuIpEp+lQ7mNnTwButm/Zl4EuMD6ucknNuPbAeoKyszLPee0VjFznJsSTHTfeqSRGRkHLKIHfOXfpG281sEVAE7AjMFskDtprZSudck6dVvomKwIwVEZFIdcogPxnn3E4g89hrMzsElDnn2jyo67QMj45xsLWHi0szT72ziEiYCul55NVtvQyPOs1YEZGIdtY98tdzzhV61dbpOjZjRUMrIhLJQrpHXtHYRfQ0oyQjwe9SRER8E9JBvq+pm5KMBGKiQ/o0RESCEtIJqBkrIiIhHOTdA8PUH+1XkItIxAvZIN/ffOzWfAW5iES2kA3yvY2asSIiAiEc5DXtvcROn0Zuyky/SxER8VXIBnlT1yDZSbFaTEJEIl7IBnlz1wCZSbF+lyEi4ruQDvJsBbmISGgGuXOO5q4BspJm+F2KiIjvQjLIu/pHGBgeI0s9chGR0Azy5u4BAAW5iAghGuRNneNBnp2sIBcRCckgb+4K9MgTFeQiIiEd5Jm62CkiEqpBPkhK3HRip0f5XYqIiO9CMsibugY0rCIiEhCSQd7SNUCWLnSKiAAhGuTjPXKNj4uIgAdBbmafMLMKM9ttZt/2oqg3MzrmaO0e1NRDEZGA6GC+2czWAtcCS5xzg2aW6U1ZJ9feM8iYQw/MEhEJCLZH/jHgW865QQDnXEvwJb25psDUQz0wS0RkXLBBfg7wNjPbZGbPmdmKk+1oZuvMrNzMyltbW8/6gM1dgwB6YJaISMAph1bM7Gkg+w3e+nLg+1OB1cAK4LdmVuycc6/f2Tm3HlgPUFZW9g/vny71yEVEXuuUQe6cu/Rk75nZx4CHA8H9ipmNAenA2Xe5T6Gla4BpBmkJ6pGLiEDwQyt/ANYCmNk5QAzQFmSbb6qpc4CMxBlETdMSbyIiEOSsFeAe4B4z2wUMAbe80bCKl5q7BzWsIiJygqCC3Dk3BLzfo1pOS3PnAAVpcZN5SBGRKS3k7uxs7tZanSIiJwqpIB8YHuVo37CmHoqInCCkgrzl+Bxy9chFRI4JqSA/NodcQS4i8nchFeTHVgbSA7NERP4uJINci0qIiPxdyAV57PRpJM0Mdvq7iEj4CKkgL8lI4NoluZjprk4RkWNCqmt708oCblpZ4HcZIiJTSkj1yEVE5B8pyEVEQpyCXEQkxCnIRURCnIJcRCTEKchFREKcglxEJMQpyEVEQpxN8Mpsb3xQs1ag5gy+JZ0JXgt0iorE847Ec4bIPO9IPGcI7rznOOcyXr/RlyA/U2ZW7pwr87uOyRaJ5x2J5wyRed6ReM4wMeetoRURkRCnIBcRCXGhEuTr/S7AJ5F43pF4zhCZ5x2J5wwTcN4hMUYuIiInFyo9chEROQkFuYhIiJvyQW5mV5jZPjOrNLM7/K5noplZvpk9a2Z7zGy3mX3K75omi5lFmdk2M3vM71omi5mlmNmDZlZhZnvN7Hy/a5oMZvaZwM/3LjO738zCbiFeM7vHzFrMbNcJ21LN7CkzOxD4PMuLY03pIDezKOCHwJXAAuA9ZrbA36om3AjwWefcAmA1cHsEnPMxnwL2+l3EJLsLeMI5Nx9YQgScv5nlAp8EypxzC4Eo4CZ/q5oQPweueN22O4C/OOfmAX8JvA7alA5yYCVQ6Zyrcs4NAQ8A1/pc04RyzjU657YGvu5m/B92rr9VTTwzywOuAu72u5bJYmbJwIXATwGcc0POuaO+FjV5ooGZZhYNxAENPtfjOefc80DH6zZfC/wi8PUvgHd6caypHuS5QO0Jr+uIgFA7xswKgWXAJp9LmQzfA74AjPlcx2QqAlqBnwWGlO42s3i/i5pozrl64D+Bw0Aj0Omc+7O/VU2aLOdcY+DrJiDLi0anepBHLDNLAB4CPu2c6/K7nolkZlcDLc65LX7XMsmigbcA/+2cWwb04tGf2lNZYFz4WsZ/kc0G4s3s/f5WNfnc+NxvT+Z/T/UgrwfyT3idF9gW1sxsOuMhfp9z7mG/65kEa4BrzOwQ48NnbzezX/lb0qSoA+qcc8f+4nqQ8WAPd5cC1c65VufcMPAwcIHPNU2WZjPLAQh8bvGi0ake5JuBeWZWZGYxjF8QedTnmiaUmRnjY6Z7nXPf8bueyeCcu9M5l+ecK2T8//Ezzrmw76E555qAWjMrDWy6BNjjY0mT5TCw2sziAj/vlxABF3kDHgVuCXx9C/CIF41Ge9HIRHHOjZjZx4EnGb+yfY9zbrfPZU20NcDNwE4z2x7Y9iXn3Ab/SpIJ9AngvkBHpQr4kM/1TDjn3CYzexDYyvgsrW2E4e36ZnY/cDGQbmZ1wH8A3wJ+a2YfZvxR3jd6cizdoi8iEtqm+tCKiIicgoJcRCTEKchFREKcglxEJMQpyEVEQpyCXEQkxCnIRURC3P8Hj06PFhNUvTQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# a logarithmic function\n",
+    "def f(x, a):\n",
+    "    return a*np.log(x)  # natural log\n",
+    "\n",
+    "# test the function\n",
+    "x = 2\n",
+    "a = 3\n",
+    "\n",
+    "# visualize using matplotlib\n",
+    "x = np.linspace(0.1, 10, 100)\n",
+    "y = [f(x_i, a) for x_i in x]\n",
+    "\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exponential functions:\n",
+    "\n",
+    "$$\n",
+    "f(x) = c\\cdot a^x\n",
+    "$$\n",
+    "\n",
+    "with $c$ and $a$ being constants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd7d8e30b90>]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# an exponential function\n",
+    "def f(x, a, c):\n",
+    "    return c*np.exp(a*x)\n",
+    "\n",
+    "# test the function\n",
+    "x = 2\n",
+    "a = 3\n",
+    "c = 4\n",
+    "\n",
+    "# visualize using matplotlib\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = [f(x_i, a, c) for x_i in x]\n",
+    "\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function Roots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check if a specific function has root. Below we are checking linear function which has a unique root of $x = -c/m$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(-1.3333333333333333, 0.0, 'x = -c/m-1.3333333333333333')"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# function to find root of a linear function\n",
+    "\n",
+    "def f(x, m, c):\n",
+    "    return m*x + c\n",
+    "\n",
+    "def has_root(x, m, c):\n",
+    "    return x == -c/m\n",
+    "\n",
+    "# generate range of values that includes root of linear function\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "\n",
+    "# add root to the list\n",
+    "x = np.append(x, -c/m)\n",
+    "\n",
+    "# order the list of values\n",
+    "x = np.sort(x)\n",
+    "\n",
+    "y = [has_root(x_i, m, c) for x_i in x]\n",
+    "\n",
+    "# y to visualize\n",
+    "y = [f(x_i, m, c) for x_i in x]\n",
+    "\n",
+    "# visualize using matplotlib, with red dots at roots\n",
+    "plt.plot(-c/m, f(-c/m, m, c), 'r.')\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "# label root value with text x = -c/m\n",
+    "plt.text(-c/m, f(-c/m, m, c), 'x = -c/m'+str(-c/m))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformations of Functions\n",
+    "\n",
+    "Transformations is an important concept where you take the output of one function and put it through another function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Shifts happen when you move the function up or down, left or right. See example below of a vertical shift:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f6fe80d0d90>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# function f(x) = x**2\n",
+    "def f(x):\n",
+    "    return x**2\n",
+    "\n",
+    "# test the function\n",
+    "x = np.linspace(-4, 4, 100)\n",
+    "\n",
+    "# visualize using matplotlib\n",
+    "plt.plot(x, f(x))\n",
+    "\n",
+    "# plot function shifted by 1 unit vertically; use red\n",
+    "plt.plot(x, f(x)+1, 'r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can do a horizontal shift:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f3403b5d2e0>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot function shifted by 2 units horizontally; use green\n",
+    "x = np.linspace(-5, 5, 100)\n",
+    "plt.plot(x, x**2, label='$f(x)=x^2$')\n",
+    "plt.plot(x, (x-2)**2, label='$f(x)=(x-2)^2$')\n",
+    "\n",
+    "plt.xlim(-6, 6)\n",
+    "plt.ylim(-2, 4)\n",
+    "\n",
+    "plt.plot(x, x*0, 'k', alpha=0.2)\n",
+    "plt.plot(x*0, x, 'k', alpha=0.2)\n",
+    "plt.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scaling transformation example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f6fe61e0430>]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# vertical scaling\n",
+    "x = np.linspace(-6, 6, 100)\n",
+    "plt.plot(x, x**2)\n",
+    "plt.plot(x, (x**2)/2, 'g')\n",
+    "\n",
+    "plt.xlim(-6, 6)\n",
+    "plt.ylim(-2, 4)\n",
+    "\n",
+    "plt.plot(x, x*0, 'k', alpha=0.2)\n",
+    "plt.plot(x*0, x, 'k', alpha=0.2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scaled by negative constant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f340844c1c0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# horizontal scaling\n",
+    "x = np.linspace(-6, 6, 100)\n",
+    "plt.plot(x, x**2)\n",
+    "plt.plot(x, -(x**2)/2, 'g')\n",
+    "\n",
+    "plt.xlim(-6, 6)\n",
+    "plt.ylim(-4, 4)\n",
+    "\n",
+    "plt.plot(x, x*0, 'k', alpha=0.2)\n",
+    "plt.plot(x*0, x, 'k', alpha=0.2)\n",
+    "\n",
+    "plt.plot(x, x**2, label='$f(x)=x^2$')\n",
+    "plt.plot(x, -(x**2)/2, 'g', label='$f(x)=-\\\\frac{1}{2}x^2$')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting sine function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f340823be80>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-6, 6, 100)\n",
+    "plt.plot(x, np.sin(x), label='$f(x)=sin(x)$')\n",
+    "\n",
+    "plt.plot(x, x*0, 'k', alpha=0.2)\n",
+    "plt.plot(x*0, x, 'k', alpha=0.2)\n",
+    "\n",
+    "plt.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another example of shifting transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f3403557160>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-6, 6, 100)\n",
+    "plt.plot(x, x**3 - x, label='$f(x)=x^3-x$')\n",
+    "\n",
+    "# horizontal shift\n",
+    "plt.plot(x, (x+2)**3 - (x+2), 'g', label='$f(x)=(x+2)^3-(x+2)$')\n",
+    "\n",
+    "plt.plot(x, x*0, 'k', alpha=0.2)\n",
+    "plt.plot(x*0, x, 'k', alpha=0.2)\n",
+    "\n",
+    "plt.ylim(-5, 5)\n",
+    "plt.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vertical scaling of sine function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f3403548490>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(-6, 6, 100)\n",
+    "\n",
+    "# sine with vertical scaling\n",
+    "plt.plot(x, np.sin(x), label='$f(x)=sin(x)$')\n",
+    "plt.plot(x, 2*np.sin(x), 'g', label='$f(x)=2sin(x)$')\n",
+    "\n",
+    "plt.plot(x, x*0, 'k', alpha=0.2)\n",
+    "plt.plot(x*0, x, 'k', alpha=0.2)\n",
+    "\n",
+    "plt.ylim(-5, 5)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercises:\n",
+    "\n",
+    "- Plot the following function: $f(x) = x^2 + 2x + 1$ and find the roots (if any) of the function.\n",
+    "- A good way to understand function is to understand more closely their properties. Play around with the function by changing values and observe how they behave. Are any of the functions related to each other? How? \n",
+    "- Write a code that applies a [sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) function to a given input."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.15 ('math')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "885f0b8c324fe4d130bb8744e2598b34a480bb115953c09edacbc3cda2096502"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/maths/feature-scaling.ipynb b/notebooks/maths/feature-scaling.ipynb
new file mode 100644
index 0000000..ac235fc
--- /dev/null
+++ b/notebooks/maths/feature-scaling.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Scaling [WIP]\n",
+    "\n",
+    "Deals with normalizing range of features or independent variables of data. In ML, feature scaling is used to \n",
+    "- ensure features contribute approximately proportionately to the final distance as opposed to one feature dominating; particularly important when euclidean distance is used\n",
+    "- help gradient descent converge faster, thus helping with the optimization problem and speeding up the learning process\n",
+    "- penalize coefficients appropriately when regularization is used as part of the loss function\n",
+    "\n",
+    "Source: [Wikipedia](https://en.wikipedia.org/wiki/Feature_scaling)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mean normalization\n",
+    "\n",
+    "Mean normalization is a method used in normalization. The formula is as follows:\n",
+    "\n",
+    "$$\n",
+    "x^{\\prime}=\\frac{x-\\operatorname{average}(x)}{\\max (x)-\\min (x)}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([-0.5352, -0.0845,  0.0423,  0.1127,  0.4648])\n",
+      "tensor([-1.4786, -0.2335,  0.1167,  0.3113,  1.2840])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "x = torch.Tensor([4, 36, 45, 50, 75])\n",
+    "x_prime = (x - x.mean()) / (x.max() - x.min()) \n",
+    "print(x_prime)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Standardization (Z-score Normalization)\n",
+    "\n",
+    "This can be used to ensure that the values of each feature in the data have zero-mean and unit-variance. The equation is as follows:\n",
+    "$$\n",
+    "x^{\\prime}=\\frac{x-\\operatorname{average}(x)}{\\operatorname{standard deviation}(x)}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([-1.4786, -0.2335,  0.1167,  0.3113,  1.2840])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "x = torch.Tensor([4, 36, 45, 50, 75])\n",
+    "x_prime = (x - x.mean()) / x.std()\n",
+    "print(x_prime)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Detailed Examples\n",
+    "Here is an illustrated example with two features. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f8a905653d0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# x_1 feature with 50 random values in range (0, 5)\n",
+    "x_1 = torch.rand(50, 1) * 5\n",
+    "x_2 = torch.rand(50, 1) * 3\n",
+    "\n",
+    "# scatter plot\n",
+    "plt.scatter(x_1.numpy(), x_2.numpy())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate and subtract mean to get zero-mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f8a907042d0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# subtract mean\n",
+    "x = torch.concat([x_1, x_2], dim=1)\n",
+    "x_new = x - x.mean(dim=0)\n",
+    "\n",
+    "# scatter plot x_new\n",
+    "plt.scatter(x_new[:, 0].numpy(), x_new[:, 1].numpy())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now normalize the variances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f8a906d1b90>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# normalize variance\n",
+    "x_new_normalized = x_new / torch.std(x_new, unbiased=False)\n",
+    "\n",
+    "# scatter plot x_new_normalized\n",
+    "plt.scatter(x_new_normalized[:, 0].numpy(), x_new_normalized[:, 1].numpy())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here is one more basic example where we reproduce the same results from this [scikit-learn tutorial](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([0.5000, 0.5000])\n",
+      "tensor([[-1., -1.],\n",
+      "        [-1., -1.],\n",
+      "        [ 1.,  1.],\n",
+      "        [ 1.,  1.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = torch.tensor([[0.0, 0.0], [0.0, 0.0], [1.0, 1.0], [1.0, 1.0]])\n",
+    "x_new = x - x.mean(dim=0)\n",
+    "print(x.mean(dim=0))\n",
+    "x_new_normalized = x_new / torch.std(x_new, unbiased=False)\n",
+    "print(x_new_normalized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "- [Wikipedia - Feature Scaling](https://en.wikipedia.org/wiki/Feature_scaling)\n",
+    "- [Wikipedia - Standard Deviation](https://en.wikipedia.org/wiki/Standard_deviation)\n",
+    "- [Normalizing Inputs by DeepLearning.AI](https://www.youtube.com/watch?v=FDCfw-YqWTE&ab_channel=DeepLearningAI)\n",
+    "- [How To Calculate the Mean and Standard Deviation — Normalizing Datasets in Pytorch](https://towardsdatascience.com/how-to-calculate-the-mean-and-standard-deviation-normalizing-datasets-in-pytorch-704bd7d05f4c)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/maths/mean.ipynb b/notebooks/maths/mean.ipynb
new file mode 100644
index 0000000..8b9e953
--- /dev/null
+++ b/notebooks/maths/mean.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean [WIP]\n",
+    "\n",
+    "Mean also referred to as the arithmetic mean is a measure of the central tendency of a set of numbers. You can calculate the mean of a set of numbers as follows:\n",
+    "\n",
+    "\n",
+    "$$\n",
+    "m=\\frac{\\text { sum of the terms }}{\\text { number of terms }}\n",
+    "$$\n",
+    "\n",
+    "or\n",
+    "\n",
+    "more formally:\n",
+    "\n",
+    "$$\n",
+    "\\bar{x}=\\frac{1}{n}\\left(\\sum_{i=1}^{n} x_{i}\\right)=\\frac{x_{1}+x_{2}+\\cdots+x_{n}}{n}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 0.7317, -1.0386, -0.4281],\n",
+      "        [ 1.1282, -0.7312,  0.2137],\n",
+      "        [-1.4916, -0.6921,  0.7739],\n",
+      "        [ 1.9643,  0.3754,  0.8449],\n",
+      "        [ 1.0241, -0.4198, -0.2965]])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.2450],\n",
+       "        [ 0.2036],\n",
+       "        [-0.4699],\n",
+       "        [ 1.0615],\n",
+       "        [ 0.1026]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "x = torch.randn(5, 3)\n",
+    "print(x)\n",
+    "\n",
+    "# use keepdim=True to preserve dimension\n",
+    "x.mean(-1, keepdim=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(42.)\n",
+      "tensor(42.)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example 2:\n",
+    "import torch\n",
+    "\n",
+    "x = torch.Tensor([4, 36, 45, 50, 75])\n",
+    "\n",
+    "print(torch.sum(x) / 5) # using sum and manual division\n",
+    "print(torch.mean(x))    # using mean function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "- [Wikipedia](https://en.wikipedia.org/wiki/Mean)\n",
+    "- [Basics of Statistics for ML Engineer](https://medium.com/technology-nineleaps/basics-of-statistics-for-machine-learning-engineers-bf2887ac716c)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/nn-lm-batch.ipynb b/notebooks/nn-lm-batch.ipynb
new file mode 100644
index 0000000..d6a25fe
--- /dev/null
+++ b/notebooks/nn-lm-batch.ipynb
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural Language Models\n",
+    "Status of Notebook: Work in Progress\n",
+    "\n",
+    "Reference: https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf\n",
+    "\n",
+    "Dynet Version: https://github.com/neubig/nn4nlp-code/blob/master/02-lm/nn-lm.py\n",
+    "\n",
+    "Old PyTorch version: https://github.com/neubig/nn4nlp-code/blob/master/02-lm-pytorch/nn-lm-batch.py\n",
+    "\n",
+    "Additions compared to `nn.lm.ipnyb`:\n",
+    "- Cleaned up model architecture code\n",
+    "- Added Dropout\n",
+    "- Using different initial learning rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import math\n",
+    "import time\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to download the datasets\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/test.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/train.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/valid.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, pro=ess each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(\" \")\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "# read the data\n",
+    "train_data = read_data('data/ptb/train.txt')\n",
+    "val_data = read_data('data/ptb/valid.txt')\n",
+    "\n",
+    "# creating the word and tag indices and special tokens\n",
+    "word_to_index = {}\n",
+    "index_to_word = {}\n",
+    "word_to_index[\"<s>\"] = len(word_to_index)\n",
+    "index_to_word[len(word_to_index)-1] = \"<s>\"\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # add <UNK> to dictionary\n",
+    "index_to_word[len(word_to_index)-1] = \"<unk>\"\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line:\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "            \n",
+    "            # has no effect because data already comes with <unk>\n",
+    "            # should work with data without <unk> already processed\n",
+    "            else: \n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(val_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield([word_to_index[word] for word in line])\n",
+    "\n",
+    "train_data = list(create_tensor(train_data))\n",
+    "val_data = list(create_tensor(val_data))\n",
+    "\n",
+    "number_of_words = len(word_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our implementation we are using batched training. There are a few differences from the original implementation found [here](https://github.com/neubig/nn4nlp-code/blob/master/02-lm/loglin-lm.py). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "\n",
+    "N = 2 # length of the n-gram\n",
+    "EMB_SIZE = 128 # size of the embedding\n",
+    "HID_SIZE = 128 # size of the hidden layer\n",
+    "\n",
+    "# Neural LM\n",
+    "class FNN_LM(nn.Module):\n",
+    "    def __init__(self, number_of_words, ngram_length, EMB_SIZE, HID_SIZE, dropout):\n",
+    "        super(FNN_LM, self).__init__()\n",
+    "\n",
+    "        # embedding layer\n",
+    "        self.embedding = nn.Embedding(number_of_words, EMB_SIZE)\n",
+    "\n",
+    "        self.fnn = nn.Sequential(\n",
+    "            # hidden layer\n",
+    "            nn.Linear(EMB_SIZE * ngram_length, HID_SIZE),\n",
+    "            nn.Tanh(),\n",
+    "            # dropout layer\n",
+    "            nn.Dropout(dropout),\n",
+    "            # output layer\n",
+    "            nn.Linear(HID_SIZE, number_of_words)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        embs = self.embedding(x)              # Size: [batch_size x num_hist x emb_size]\n",
+    "        feat = embs.view(embs.size(0), -1)    # Size: [batch_size x (num_hist*emb_size)]\n",
+    "        logit = self.fnn(feat)                # Size: batch_size x num_words                    \n",
+    "        return logit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Settings and Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = FNN_LM(number_of_words, N, EMB_SIZE, HID_SIZE, dropout=0.2)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
+    "criterion = torch.nn.CrossEntropyLoss(reduction=\"sum\")\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "\n",
+    "# function to calculate the sentence loss\n",
+    "def calc_sent_loss(sent):\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    \n",
+    "    # initial history is equal to end of sentence symbols\n",
+    "    hist = [S] * N\n",
+    "    \n",
+    "    # collect all target and histories\n",
+    "    all_targets = []\n",
+    "    all_histories = []\n",
+    "    \n",
+    "    # step through the sentence, including the end of sentence token\n",
+    "    for next_word in sent + [S]:\n",
+    "        all_histories.append(list(hist))\n",
+    "        all_targets.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "\n",
+    "    logits = model(torch.LongTensor(all_histories).to(device))\n",
+    "    loss = criterion(logits, torch.LongTensor(all_targets).to(device))\n",
+    "\n",
+    "    return loss\n",
+    "\n",
+    "MAX_LEN = 100\n",
+    "# Function to generate a sentence\n",
+    "def generate_sent():\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    hist = [S] * N\n",
+    "    sent = []\n",
+    "    while True:\n",
+    "        logits = model(torch.LongTensor([hist]).to(device))\n",
+    "        p = torch.nn.functional.softmax(logits) # 1 x number_of_words\n",
+    "        next_word = p.multinomial(num_samples=1).item()\n",
+    "        if next_word == S or len(sent) == MAX_LEN:\n",
+    "            break\n",
+    "        sent.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "    return sent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--finished 5000 sentences (words/sec=12807.67)\n",
+      "--finished 10000 sentences (words/sec=12788.71)\n",
+      "--finished 15000 sentences (words/sec=12807.44)\n",
+      "--finished 20000 sentences (words/sec=12801.59)\n",
+      "--finished 25000 sentences (words/sec=12852.69)\n",
+      "--finished 30000 sentences (words/sec=12843.39)\n",
+      "--finished 35000 sentences (words/sec=12835.04)\n",
+      "--finished 40000 sentences (words/sec=12816.01)\n",
+      "iter 0: train loss/word=6.1274, ppl=458.2398, (words/sec=12801.17)\n",
+      "iter 0: dev loss/word=5.8676, ppl=353.3835, (words/sec=1.44s)\n",
+      "it will change at georgia & co. got instead of totally a appointment from the big bankers posted <unk> & co. also received that brokers\n",
+      "one and claim the politicians amount for <unk> the measure of the california santa contract\n",
+      "our birth capitol led the giant <unk> by an <unk> market in the central <unk> held the rise of the company 's sheet that the irs on britain dollars\n",
+      "yesterday 's jail & <unk> investigations on news for buying creditors has lower market for polish statement so and now in a bill to government americans system to my march and programs of links stock-market program charlotte nasdaq lowest judge <unk> provide an final state university of foot an woman spokesman for something he was very constitution on the new post\n",
+      "it N also buy-out of bank in may industry mr. phelan said <unk> in his current N N owned of closely bartlett below minister blocking which mr. repeat and unemployment to de our own news to submit resolution trust said a rivals on the reached which now end for most cautioned failed more than N remic mortgage officials and and goldman sachs & co. currently myself mrs. mandatory almost <unk> to the hoffman <unk> greater silver kidder peabody & co. does n't <unk> any grip from the buy-out change in drexel 's third-quarter profit of N N to speculation a\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/nlp/lib/python3.7/site-packages/ipykernel_launcher.py:38: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--finished 5000 sentences (words/sec=12587.62)\n",
+      "--finished 10000 sentences (words/sec=12652.41)\n",
+      "--finished 15000 sentences (words/sec=12740.18)\n",
+      "--finished 20000 sentences (words/sec=12763.71)\n",
+      "--finished 25000 sentences (words/sec=12753.94)\n",
+      "--finished 30000 sentences (words/sec=12754.24)\n",
+      "--finished 35000 sentences (words/sec=12762.18)\n",
+      "--finished 40000 sentences (words/sec=12740.41)\n",
+      "iter 1: train loss/word=5.7389, ppl=310.7324, (words/sec=12744.21)\n",
+      "iter 1: dev loss/word=5.7766, ppl=322.6629, (words/sec=1.40s)\n",
+      "the advertising for champion the dollar was named whose damage was down from lawyers and the new england told them need\n",
+      "rumors with cents a share\n",
+      "justice general operations in chicago\n",
+      "british bought what of going to pay to rates since april\n",
+      "according to an <unk> family\n",
+      "--finished 5000 sentences (words/sec=12702.39)\n",
+      "--finished 10000 sentences (words/sec=12731.82)\n",
+      "--finished 15000 sentences (words/sec=12755.89)\n",
+      "--finished 20000 sentences (words/sec=12828.83)\n",
+      "--finished 25000 sentences (words/sec=12836.63)\n",
+      "--finished 30000 sentences (words/sec=12801.01)\n",
+      "--finished 35000 sentences (words/sec=12803.18)\n",
+      "--finished 40000 sentences (words/sec=12779.24)\n",
+      "iter 2: train loss/word=5.5996, ppl=270.3145, (words/sec=12792.34)\n",
+      "iter 2: dev loss/word=5.7464, ppl=313.0468, (words/sec=1.40s)\n",
+      "french his experience within george bush expected luxury world culture by planning\n",
+      "complete as example this scheduled other sellers operations\n",
+      "much of the retail\n",
+      "just scheduled time to foreign exchange cigarette merchandise outlets in the market 's $ N a share a year earlier branch in the turmoil before our company\n",
+      "the manufacturing products construction <unk> has about $ N million or $ N million from $ N million in bridge\n",
+      "--finished 5000 sentences (words/sec=12953.86)\n",
+      "--finished 10000 sentences (words/sec=12970.24)\n",
+      "--finished 15000 sentences (words/sec=12896.14)\n",
+      "--finished 20000 sentences (words/sec=12875.42)\n",
+      "--finished 25000 sentences (words/sec=12833.31)\n",
+      "--finished 30000 sentences (words/sec=12839.11)\n",
+      "--finished 35000 sentences (words/sec=12822.49)\n",
+      "--finished 40000 sentences (words/sec=12814.87)\n",
+      "iter 3: train loss/word=5.5124, ppl=247.7381, (words/sec=12819.57)\n",
+      "iter 3: dev loss/word=5.7235, ppl=305.9709, (words/sec=1.39s)\n",
+      "ago\n",
+      "british community interest reserves in a low $ N a tax ministry in japan and wants to say it shows the consumer price network\n",
+      "expects operations for this first maryland staff studies in beijing\n",
+      "u.s. government bills here have been implemented\n",
+      "computer software inc. and expected that were out the <unk> moscow who illegally a <unk> <unk> stage sets for over $ N million a year earlier\n",
+      "--finished 5000 sentences (words/sec=12860.84)\n",
+      "--finished 10000 sentences (words/sec=12756.10)\n",
+      "--finished 15000 sentences (words/sec=12795.20)\n",
+      "--finished 20000 sentences (words/sec=12799.80)\n",
+      "--finished 25000 sentences (words/sec=12830.27)\n",
+      "--finished 30000 sentences (words/sec=12820.51)\n",
+      "--finished 35000 sentences (words/sec=12821.23)\n",
+      "--finished 40000 sentences (words/sec=12839.05)\n",
+      "iter 4: train loss/word=5.4502, ppl=232.8159, (words/sec=12841.22)\n",
+      "iter 4: dev loss/word=5.7149, ppl=303.3545, (words/sec=1.40s)\n",
+      "but the pilots are not profitable if mr. <unk> said\n",
+      "his trading know that cathay is through the research session mr. brooks and center usually raised its quarterly dividend\n",
+      "and <unk> <unk> kept a <unk> turn on the moment he said\n",
+      "but a church premium still the stockholders die\n",
+      "if they benefited from the san couple has a china 's strategy while government prices\n"
+     ]
+    }
+   ],
+   "source": [
+    "# start training\n",
+    "for ITER in range(5):\n",
+    "    # training\n",
+    "    random.shuffle(train_data)\n",
+    "    model.train()\n",
+    "    train_words, train_loss = 0, 0.0\n",
+    "    start = time.time()\n",
+    "    for sent_id, sent in enumerate(train_data):        \n",
+    "        my_loss = calc_sent_loss(sent)\n",
+    "        train_loss += my_loss.item()\n",
+    "        train_words += len(sent)\n",
+    "        optimizer.zero_grad()\n",
+    "        my_loss.backward()\n",
+    "        optimizer.step()\n",
+    "        if (sent_id+1) % 5000 == 0:\n",
+    "            print(\"--finished %r sentences (words/sec=%.2f)\" % (sent_id+1, train_words/(time.time()-start)))\n",
+    "    print(\"iter %r: train loss/word=%.4f, ppl=%.4f, (words/sec=%.2f)\" % (ITER, train_loss/train_words, math.exp(train_loss/train_words), train_words/(time.time()-start)))\n",
+    "\n",
+    "    # evaluation\n",
+    "    model.eval()\n",
+    "    dev_words, dev_loss = 0, 0.0\n",
+    "    start = time.time()\n",
+    "    for sent_id, sent in enumerate(val_data):\n",
+    "        my_loss = calc_sent_loss(sent)\n",
+    "        dev_loss += my_loss.item()\n",
+    "        dev_words += len(sent)\n",
+    "    print(\"iter %r: dev loss/word=%.4f, ppl=%.4f, (words/sec=%.2fs)\" % (ITER, dev_loss/dev_words, math.exp(dev_loss/dev_words), time.time()-start))\n",
+    "\n",
+    "    # Generate a few sentences\n",
+    "    for _ in range(5):\n",
+    "        sent = generate_sent()\n",
+    "        print(\" \".join([index_to_word[x] for x in sent]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/nn-lm.ipynb b/notebooks/nn-lm.ipynb
new file mode 100644
index 0000000..283f688
--- /dev/null
+++ b/notebooks/nn-lm.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural Language Models\n",
+    "Status of Notebook: Work in Progress\n",
+    "\n",
+    "Reference: https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf\n",
+    "\n",
+    "Dynet Version: https://github.com/neubig/nn4nlp-code/blob/master/02-lm/nn-lm.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import math\n",
+    "import time\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment to download the datasets\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/test.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/train.txt\n",
+    "#!wget https://raw.githubusercontent.com/neubig/nn4nlp-code/master/data/ptb/valid.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to read in data, pro=ess each line and split columns by \" ||| \"\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(\" \")\n",
+    "            data.append(line)\n",
+    "    return data\n",
+    "\n",
+    "# read the data\n",
+    "train_data = read_data('data/ptb/train.txt')\n",
+    "val_data = read_data('data/ptb/valid.txt')\n",
+    "\n",
+    "# creating the word and tag indices and special tokens\n",
+    "word_to_index = {}\n",
+    "index_to_word = {}\n",
+    "word_to_index[\"<s>\"] = len(word_to_index)\n",
+    "index_to_word[len(word_to_index)-1] = \"<s>\"\n",
+    "word_to_index[\"<unk>\"] = len(word_to_index) # add <UNK> to dictionary\n",
+    "index_to_word[len(word_to_index)-1] = \"<unk>\"\n",
+    "\n",
+    "# create word to index dictionary and tag to index dictionary from data\n",
+    "def create_dict(data, check_unk=False):\n",
+    "    for line in data:\n",
+    "        for word in line:\n",
+    "            if check_unk == False:\n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = len(word_to_index)\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "            \n",
+    "            # has no effect because data already comes with <unk>\n",
+    "            # should work with data without <unk> already processed\n",
+    "            else: \n",
+    "                if word not in word_to_index:\n",
+    "                    word_to_index[word] = word_to_index[\"<unk>\"]\n",
+    "                    index_to_word[len(word_to_index)-1] = word\n",
+    "\n",
+    "create_dict(train_data)\n",
+    "create_dict(val_data, check_unk=True)\n",
+    "\n",
+    "# create word and tag tensors from data\n",
+    "def create_tensor(data):\n",
+    "    for line in data:\n",
+    "        yield([word_to_index[word] for word in line])\n",
+    "\n",
+    "train_data = list(create_tensor(train_data))\n",
+    "val_data = list(create_tensor(val_data))\n",
+    "\n",
+    "number_of_words = len(word_to_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our implementation we are using batched training. There are a few differences from the original implementation found [here](https://github.com/neubig/nn4nlp-code/blob/master/02-lm/loglin-lm.py). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## define the model\n",
+    "\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "\n",
+    "N = 2 # length of the n-gram\n",
+    "EMB_SIZE = 128 # size of the embedding\n",
+    "HID_SIZE = 128 # size of the hidden layer\n",
+    "\n",
+    "# Neural LM\n",
+    "class NeuralLM(nn.Module):\n",
+    "    def __init__(self, number_of_words, ngram_length, EMB_SIZE, HID_SIZE):\n",
+    "        super(NeuralLM, self).__init__()\n",
+    "\n",
+    "        # embedding layer\n",
+    "        self.embedding = nn.Embedding(number_of_words, EMB_SIZE)\n",
+    "\n",
+    "        # hidden layer\n",
+    "        self.hidden = nn.Linear(EMB_SIZE * ngram_length, HID_SIZE)\n",
+    "        # output layer\n",
+    "        self.output = nn.Linear(HID_SIZE, number_of_words)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        embs = self.embedding(x)                        # Size: [batch_size x num_hist x emb_size]\n",
+    "        embs = embs.view(embs.size(0), -1)              # Size: [batch_size x (num_hist*emb_size)]\n",
+    "        h = torch.nn.functional.tanh(self.hidden(embs)) # Size: [batch_size x hid_size]\n",
+    "        scores = self.output(h)                         # Size: batch_size x num_words\n",
+    "        return scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Settings and Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = NeuralLM(number_of_words, N, EMB_SIZE, HID_SIZE)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    model.to(device)\n",
+    "\n",
+    "# function to calculate the sentence loss\n",
+    "def calc_sent_loss(sent):\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    \n",
+    "    # initial history is equal to end of sentence symbols\n",
+    "    hist = [S] * N\n",
+    "    \n",
+    "    # collect all target and histories\n",
+    "    all_targets = []\n",
+    "    all_histories = []\n",
+    "    \n",
+    "    # step through the sentence, including the end of sentence token\n",
+    "    for next_word in sent + [S]:\n",
+    "        all_histories.append(list(hist))\n",
+    "        all_targets.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "\n",
+    "    logits = model(torch.LongTensor(all_histories).to(device))\n",
+    "    loss = criterion(logits, torch.LongTensor(all_targets).to(device))\n",
+    "\n",
+    "    return loss\n",
+    "\n",
+    "MAX_LEN = 100\n",
+    "# Function to generate a sentence\n",
+    "def generate_sent():\n",
+    "    S = word_to_index[\"<s>\"]\n",
+    "    hist = [S] * N\n",
+    "    sent = []\n",
+    "    while True:\n",
+    "        logits = model(torch.LongTensor([hist]).to(device))\n",
+    "        p = torch.nn.functional.softmax(logits) # 1 x number_of_words\n",
+    "        next_word = p.multinomial(num_samples=1).item()\n",
+    "        if next_word == S or len(sent) == MAX_LEN:\n",
+    "            break\n",
+    "        sent.append(next_word)\n",
+    "        hist = hist[1:] + [next_word]\n",
+    "    return sent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 0: train loss/word=4.1802, ppl=65.3775\n",
+      "iter 0: dev loss/word=4.4128, ppl=82.4961, time=1.26s\n",
+      "in constitution physics which could counting suspect include be on\n",
+      "dealers manufacturers plans commissions\n",
+      "in constitution physics which could counting suspect include be on behalf he declares\n",
+      "in constitution physics which could counting suspect include be and which and an for was designed on themes of weakness jobs n't be and which <unk> developed the sale such from about other objectives\n",
+      "N have in prolonged damage\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/nlp/lib/python3.7/site-packages/ipykernel_launcher.py:38: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 1: train loss/word=4.4307, ppl=83.9873\n",
+      "iter 1: dev loss/word=4.5315, ppl=92.8970, time=1.27s\n",
+      "two <unk> hours <unk> relations clark new an index big were medicine more 'm bank N in october this fall\n",
+      "two this said its consumer puts for democratic futures the ringers out N note a day to get this affairs\n",
+      "this time\n",
+      "two this said its consumer puts for democratic futures the ringers out N note a top outstanding bank for $ candidates savings <unk> relationship in shopping for declared futures the ringers out N note a day to get this said its consumer puts to highlight this fall\n",
+      "this time the\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 2: train loss/word=4.4670, ppl=87.0953\n",
+      "iter 2: dev loss/word=4.5699, ppl=96.5306, time=1.28s\n",
+      "there is by being it on the first was estimated for possible the experiment of those after the key\n",
+      "there is by\n",
+      "<unk> intensity excess $ co. spot N N N N N the tanker of those after in <unk> forced around who participated in <unk> forced around to $ N million wednesday\n",
+      "there is by\n",
+      "to one why N gallons iii bush from $ N million wednesday\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 3: train loss/word=4.4909, ppl=89.1985\n",
+      "iter 3: dev loss/word=4.5530, ppl=94.9163, time=1.31s\n",
+      "in of western actions it does about service pilots a the company costs there with chief executive retailing <unk> under and are will for which the department showed N N of stock funds profit as well a buildup and an interest has expects <unk> up in the friday-the-13th third\n",
+      "in of <unk> and the products costs has about shareholders fidelity with agreed was it to a less and stock will okla. say the former economic to make\n",
+      "in it does about service pilots a the company costs there with a five-year and then <unk> more\n",
+      "in of western actions it does about service pilots at profit common runs has\n",
+      "thus declined to comment\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 4: train loss/word=4.4966, ppl=89.7113\n",
+      "iter 4: dev loss/word=4.6409, ppl=103.6412, time=1.28s\n",
+      "the apparent centers groups by to reform\n",
+      "are consumers too deep over that we do n't want to continue owning stocks we oct. in <unk>\n",
+      "the apparent centers groups by the <unk> missile n't available five former corp to slow owning u.k. <unk>\n",
+      "the apparent centers groups by the and centers but to limit owning investment\n",
+      "the apparent centers groups by the to share mr. lehman attributed n't available five former corp to slow owning it was who i was coast to round owning revenue to specific another dramatic worked and financial announcements <unk> wo n't end anytime soon\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 5: train loss/word=4.5213, ppl=91.9530\n",
+      "iter 5: dev loss/word=4.7837, ppl=119.5463, time=1.28s\n",
+      "the other involving plant the commission value to consolidate several lawsuits <unk> senior the commission value to consolidate several lawsuits <unk> senior the commission value to consolidate several lawsuits in many other say central\n",
+      "the other involving plant the commission value to consolidate several lawsuits <unk> senior the commission value to consolidate several lawsuits is for filled <unk> senior the commission value to consolidate several lawsuits in many other say central\n",
+      "the other involving plant the commission groups\n",
+      "the other involving plant the commission value of leading funds code growth channel grows aide market against her for clearance 's <unk> of the <unk> era of the <unk> era of the <unk> era of the <unk> era of the <unk> era to mr. a ratio process some air fares about rumors <unk> <unk> process the <unk> era of the <unk> era the <unk> era of the <unk> era of the <unk> era of the <unk> era of the <unk> era of the <unk> era of the <unk> era of the <unk> era to mr. the <unk> era to mr.\n",
+      "the other involving plant the commission value bank leading some continental a senior the commission value to consolidate several lawsuits on fetch for violations <unk> senior the commission value to consolidate several lawsuits <unk> senior the commission value to consolidate several lawsuits <unk> senior the commission value in draw other time strict fire american express for two operations n't if debt only on behalf n't increase he also questioned for two operations n't if debt\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 6: train loss/word=4.5284, ppl=92.6074\n",
+      "iter 6: dev loss/word=4.8860, ppl=132.4199, time=1.27s\n",
+      "toyota have expressed recent durable attempt <unk> chief stocks spend notes\n",
+      "toyota have expressed recent durable attempt <unk> development\n",
+      "toyota have expressed recent durable attempt to\n",
+      "toyota have expressed recent durable attempt <unk> development 's also <unk> concedes stocks could back average resolve by\n",
+      "toyota of N <unk> occurred stocks less today <unk>\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 7: train loss/word=4.5339, ppl=93.1220\n",
+      "iter 7: dev loss/word=4.9127, ppl=136.0103, time=1.28s\n",
+      "the distributor environment wealth fleet mosbacher N N from turnover citing commitment place the partnership more than year <unk> <unk> new york $ N to N a share a year earlier assets at fairly insurance will open and assets <unk> subsidiaries by and mae <unk> and his wife by fannie mae N N from turnover citing commitment place the partnership more than a partial on <unk> changes by of companies and assets <unk> subsidiaries in a trading range N N from turnover citing commitment place the partnership more than year <unk> <unk> new york $ N to N to N\n",
+      "the distributor environment wealth fleet mosbacher N N from turnover citing commitment place only will open to represent this week\n",
+      "the distributor environment wealth fleet mosbacher N N from turnover citing commitment place only will open\n",
+      "the distributor environment wealth fleet mosbacher N N from turnover citing commitment place the partnership more than year <unk> <unk> new york $ N to N N from turnover citing commitment place only will open and assets <unk> subsidiaries that not officials monday N N from turnover citing commitment place the partnership more other <unk> match by by mae <unk> and <unk> and his wife by fannie mae N N from turnover citing commitment place the partnership more than a partial of refusing and assets <unk> subsidiaries by and mae <unk> and his wife by fannie mae N N from\n",
+      "the distributor environment wealth fleet mosbacher N N from turnover citing commitment place only will open to represent this week\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n",
+      "--finished 20000 sentences\n",
+      "--finished 25000 sentences\n",
+      "--finished 30000 sentences\n",
+      "--finished 35000 sentences\n",
+      "--finished 40000 sentences\n",
+      "iter 8: train loss/word=4.5402, ppl=93.7127\n",
+      "iter 8: dev loss/word=4.9000, ppl=134.2900, time=1.28s\n",
+      "barney any projections case for purchase\n",
+      "barney any projections case for purchase about liability says fast financial profit would increase close sassy cie are into imports the will concern the work industry did doing to record other cash and the but section N <unk> N gorbachev games the continued mr. sohmer says <unk> for N N aided executive into attacking and increased risks <unk> where with ratings pitch democratic\n",
+      "barney any projections case for purchase\n",
+      "barney any projections case for purchase\n",
+      "barney any projections case for purchase\n",
+      "--finished 5000 sentences\n",
+      "--finished 10000 sentences\n",
+      "--finished 15000 sentences\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_14352/404430955.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0msent_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# CHANGE to all train_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mmy_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalc_sent_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mtrain_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmy_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_14352/2289298869.py\u001b[0m in \u001b[0;36mcalc_sent_loss\u001b[0;34m(sent)\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnext_word\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLongTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_histories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLongTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_targets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/envs/nlp/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1108\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1109\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1111\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_14352/2217491764.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0membs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# [batch_size x num_hist x emb_size]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0membs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# [batch_size x (num_hist*emb_size)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhidden\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# batch_size x hid_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# batch_size x num_words\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# start training\n",
+    "for ITER in range (10): # CHANGE to 100\n",
+    "    # training\n",
+    "    random.shuffle(train_data)\n",
+    "\n",
+    "    model.train()\n",
+    "    train_words, train_loss = 0, 0.0\n",
+    "    for sent_id, sent in enumerate(train_data): # CHANGE to all train_data\n",
+    "        \n",
+    "        my_loss = calc_sent_loss(sent)\n",
+    "        \n",
+    "        train_loss += my_loss.item()\n",
+    "        train_words += len(sent)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        my_loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if (sent_id+1) % 5000 == 0:\n",
+    "            print(\"--finished %r sentences\" % (sent_id+1))\n",
+    "    print(\"iter %r: train loss/word=%.4f, ppl=%.4f\" % (ITER, train_loss/train_words, math.exp(train_loss/train_words)))\n",
+    "\n",
+    "    # evaluation\n",
+    "    model.eval()\n",
+    "    dev_words, dev_loss = 0, 0.0\n",
+    "    start = time.time()\n",
+    "    for sent_id, sent in enumerate(val_data):\n",
+    "        my_loss = calc_sent_loss(sent)\n",
+    "        dev_loss += my_loss.item()\n",
+    "        dev_words += len(sent)\n",
+    "    print(\"iter %r: dev loss/word=%.4f, ppl=%.4f, time=%.2fs\" % (ITER, dev_loss/dev_words, math.exp(dev_loss/dev_words), time.time()-start))\n",
+    "\n",
+    "    # Generate a few sentences\n",
+    "    for _ in range(5):\n",
+    "        sent = generate_sent()\n",
+    "        print(\" \".join([index_to_word[x] for x in sent]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nlp",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/nn_from_scratch.ipynb b/notebooks/nn_from_scratch.ipynb
new file mode 100644
index 0000000..fdb2bd9
--- /dev/null
+++ b/notebooks/nn_from_scratch.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Simple Neural Network from Scratch with PyTorch and Google Colab\n",
+    "\n",
+    "In this tutorial we implement a simple neural network from scratch using PyTorch.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About\n",
+    "\n",
+    "In this tutorial we will implement a simple neural network from scratch using PyTorch. The idea of the tutorial is to teach you the basics of PyTorch and how it can be used to implement a neural network from scratch. I will go over some of the basic functionalities and concepts available in PyTorch that will allow you to build your own neural networks. \n",
+    "\n",
+    "This tutorial assumes you have prior knowledge of how a neural network works. Don’t worry! Even if you are not so sure, you will be okay. For advanced PyTorch users, this tutorial may still serve as a refresher. This tutorial is heavily inspired by this [Neural Network implementation](https://repl.it/talk/announcements/Build-a-Neural-Network-in-Python/5457) coded purely using Numpy. In fact, I tried re-implementing the code using PyTorch instead and added my own intuitions and explanations. Thanks to [Samay](https://repl.it/@shamdasani) for his phenomenal work, I hope this inspires many others as it did with me.\n",
+    "\n",
+    "The `torch` module provides all the necessary **tensor** operators you will need to implement your first neural network from scratch in PyTorch. That's right! In PyTorch everything is a Tensor, so this is the first thing you will need to get used to. Let's import the libraries we will need for this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "Let's start by creating some sample data using the `torch.tensor` command. In Numpy, this could be done with `np.array`. Both functions serve the same purpose, but in PyTorch everything is a Tensor as opposed to a vector or matrix. We define types in PyTorch using the `dtype=torch.xxx` command. \n",
+    "\n",
+    "In the data below, `X` represents the amount of hours studied and how much time students spent sleeping, whereas `y` represent grades. The variable `xPredicted` is a single input for which we want to predict a grade using the parameters learned by the neural network. Remember, the neural network wants to learn a mapping between `X` and `y`, so it will try to take a guess from what it has learned from the training data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = torch.tensor(([2, 9], [1, 5], [3, 6]), dtype=torch.float) # 3 X 2 tensor\n",
+    "y = torch.tensor(([92], [100], [89]), dtype=torch.float) # 3 X 1 tensor\n",
+    "xPredicted = torch.tensor(([4, 8]), dtype=torch.float) # 1 X 2 tensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check the size of the tensors we have just created with the `size` command. This is equivalent to the `shape` command used in tools such as Numpy and Tensorflow. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 2])\n",
+      "torch.Size([3, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X.size())\n",
+    "print(y.size())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scaling\n",
+    "\n",
+    "Below we are performing some scaling on the sample data. Notice that the `max` function returns both a tensor and the corresponding indices. So we use `_` to capture the indices which we won't use here because we are only interested in the max values to conduct the scaling. Perfect! Our data is now in a very nice format our neural network will appreciate later on. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([0.5000, 1.0000])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# scale units\n",
+    "X_max, _ = torch.max(X, 0)\n",
+    "xPredicted_max, _ = torch.max(xPredicted, 0)\n",
+    "\n",
+    "X = torch.div(X, X_max)\n",
+    "xPredicted = torch.div(xPredicted, xPredicted_max)\n",
+    "y = y / 100  # max test score is 100\n",
+    "print(xPredicted)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that there are two functions `max` and `div` that I didn't discuss above. They do exactly what they imply: `max` finds the maximum value in a vector... I mean tensor; and `div` is basically a nice little function to divide two tensors. \n",
+    "\n",
+    "## Model (Computation Graph)\n",
+    "Once the data has been processed and it is in the proper format, all you need to do now is to define your model. Here is where things begin to change a little as compared to how you would build your neural networks using, say, something like Keras or Tensorflow. However, you will realize quickly as you go along that PyTorch doesn't differ much from other deep learning tools. At the end of the day we are constructing a computation graph, which is used to dictate how data should flow and what type of operations are performed on this information. \n",
+    "\n",
+    "For illustration purposes, we are building the following neural network or computation graph:\n",
+    "\n",
+    "\n",
+    "![alt text](https://drive.google.com/uc?export=view&id=1l-sKpcCJCEUJV1BlAqcVAvLXLpYCInV6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Neural_Network(nn.Module):\n",
+    "    def __init__(self, ):\n",
+    "        super(Neural_Network, self).__init__()\n",
+    "        # parameters\n",
+    "        # TODO: parameters can be parameterized instead of declaring them here\n",
+    "        self.inputSize = 2\n",
+    "        self.outputSize = 1\n",
+    "        self.hiddenSize = 3\n",
+    "        \n",
+    "        # weights\n",
+    "        self.W1 = torch.randn(self.inputSize, self.hiddenSize) # 3 X 2 tensor\n",
+    "        self.W2 = torch.randn(self.hiddenSize, self.outputSize) # 3 X 1 tensor\n",
+    "        \n",
+    "    def forward(self, X):\n",
+    "        self.z = torch.matmul(X, self.W1) # 3 X 3 \".dot\" does not broadcast in PyTorch\n",
+    "        self.z2 = self.sigmoid(self.z) # activation function\n",
+    "        self.z3 = torch.matmul(self.z2, self.W2)\n",
+    "        o = self.sigmoid(self.z3) # final activation function\n",
+    "        return o\n",
+    "        \n",
+    "    def sigmoid(self, s):\n",
+    "        return 1 / (1 + torch.exp(-s))\n",
+    "    \n",
+    "    def sigmoidPrime(self, s):\n",
+    "        # derivative of sigmoid\n",
+    "        return s * (1 - s)\n",
+    "    \n",
+    "    def backward(self, X, y, o):\n",
+    "        self.o_error = y - o # error in output\n",
+    "        self.o_delta = self.o_error * self.sigmoidPrime(o) # derivative of sig to error\n",
+    "        self.z2_error = torch.matmul(self.o_delta, torch.t(self.W2))\n",
+    "        self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)\n",
+    "        self.W1 += torch.matmul(torch.t(X), self.z2_delta)\n",
+    "        self.W2 += torch.matmul(torch.t(self.z2), self.o_delta)\n",
+    "        \n",
+    "    def train(self, X, y):\n",
+    "        # forward + backward pass for training\n",
+    "        o = self.forward(X)\n",
+    "        self.backward(X, y, o)\n",
+    "        \n",
+    "    def saveWeights(self, model):\n",
+    "        # we will use the PyTorch internal storage functions\n",
+    "        torch.save(model, \"NN\")\n",
+    "        # you can reload model with all the weights and so forth with:\n",
+    "        # torch.load(\"NN\")\n",
+    "        \n",
+    "    def predict(self):\n",
+    "        print (\"Predicted data based on trained weights: \")\n",
+    "        print (\"Input (scaled): \\n\" + str(xPredicted))\n",
+    "        print (\"Output: \\n\" + str(self.forward(xPredicted)))\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the purpose of this tutorial, we are not going to be talking math stuff, that's for another day. I just want you to get a gist of what it takes to build a neural network from scratch using PyTorch. Let's break down the model which was declared via the class above. \n",
+    "\n",
+    "## Class Header\n",
+    "First, we defined our model via a class because that is the recommended way to build the computation graph. The class header contains the name of the class `Neural Network` and the parameter `nn.Module` which basically indicates that we are defining our own neural network. \n",
+    "\n",
+    "```python\n",
+    "class Neural_Network(nn.Module):\n",
+    "```\n",
+    "\n",
+    "## Initialization\n",
+    "The next step is to define the initializations ( `def __init__(self,)`) that will be performed upon creating an instance of the customized neural network. You can declare the parameters of your model here, but typically, you would declare the structure of your network in this section -- the size of the hidden layers and so forth. Since we are building the neural network from scratch, we explicitly declared the size of the weights matrices: one that stores the parameters from the input to hidden layer; and one that stores the parameter from the hidden to output layer. Both weight matrices are initialized with values randomly chosen from a normal distribution via `torch.randn(...)`. Note that we are not using bias just to keep things as simple as possible.  \n",
+    "\n",
+    "```python\n",
+    "def __init__(self, ):\n",
+    "    super(Neural_Network, self).__init__()\n",
+    "    # parameters\n",
+    "    # TODO: parameters can be parameterized instead of declaring them here\n",
+    "    self.inputSize = 2\n",
+    "    self.outputSize = 1\n",
+    "    self.hiddenSize = 3\n",
+    "\n",
+    "    # weights\n",
+    "    self.W1 = torch.randn(self.inputSize, self.hiddenSize) # 3 X 2 tensor\n",
+    "    self.W2 = torch.randn(self.hiddenSize, self.outputSize) # 3 X 1 tensor\n",
+    "```\n",
+    "\n",
+    "## The Forward Function\n",
+    "The `forward` function is where all the magic happens (see below). This is where the data enters and is fed into the computation graph (i.e., the neural network structure we have built). Since we are building a simple neural network with one hidden layer, our forward function looks very simple:\n",
+    "\n",
+    "```python\n",
+    "def forward(self, X):\n",
+    "    self.z = torch.matmul(X, self.W1) \n",
+    "    self.z2 = self.sigmoid(self.z) # activation function\n",
+    "    self.z3 = torch.matmul(self.z2, self.W2)\n",
+    "    o = self.sigmoid(self.z3) # final activation function\n",
+    "    return o\n",
+    "```\n",
+    "\n",
+    "The `forward` function above takes the input `X`and then performs a matrix multiplication (`torch.matmul(...)`) with the first weight matrix `self.W1`. Then the result is applied an activation function, `sigmoid`. The resulting matrix of the activation is then multiplied with the second weight matrix `self.W2`. Then another activation if performed, which renders the output of the neural network or computation graph. The process I described above is simply what's known as a `feedforward pass`. In order for the weights to optimize when training, we need a backpropagation algorithm. \n",
+    "\n",
+    "## The Backward Function\n",
+    "The `backward` function contains the backpropagation algorithm, where the goal is to essentially minimize the loss with respect to our weights. In other words, the weights need to be updated in such  a way that the loss decreases while the neural network is training (well, that is what we hope for). All this magic is possible with the gradient descent algorithm which is declared in the `backward` function. Take a minute or two to inspect what is happening in the code below:\n",
+    "\n",
+    "```python\n",
+    "def backward(self, X, y, o):\n",
+    "    self.o_error = y - o # error in output\n",
+    "    self.o_delta = self.o_error * self.sigmoidPrime(o) \n",
+    "    self.z2_error = torch.matmul(self.o_delta, torch.t(self.W2))\n",
+    "    self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)\n",
+    "    self.W1 += torch.matmul(torch.t(X), self.z2_delta)\n",
+    "    self.W2 += torch.matmul(torch.t(self.z2), self.o_delta)\n",
+    "```\n",
+    "\n",
+    "Notice that we are performing a lot of matrix multiplications along with the transpose operations via the `torch.matmul(...)` and `torch.t(...)` operations, respectively. The rest is simply gradient descent -- there is nothing to it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "All that is left now is to train the neural network. First we create an instance of the computation graph we have just built:\n",
+    "\n",
+    "```python\n",
+    "NN = Neural_Network()\n",
+    "```\n",
+    "\n",
+    "Then we train the model for `1000` rounds. Notice that in PyTorch `NN(X)` automatically calls the `forward` function so there is no need to explicitly call `NN.forward(X)`. \n",
+    "\n",
+    "After we have obtained the predicted output for ever round of training, we compute the loss, with the following code:\n",
+    "\n",
+    "```python\n",
+    "torch.mean((y - NN(X))**2).detach().item()\n",
+    "```\n",
+    "\n",
+    "The next step is to start the training (foward + backward) via `NN.train(X, y)`. After we have trained the neural network, we can store the model and output the predicted value of the single instance we declared in the beginning, `xPredicted`.  \n",
+    "\n",
+    "Let's train!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#0 Loss: 0.23507122695446014\n",
+      "#100 Loss: 0.003401519963517785\n",
+      "#200 Loss: 0.0031734900549054146\n",
+      "#300 Loss: 0.0030537480488419533\n",
+      "#400 Loss: 0.0029342835769057274\n",
+      "#500 Loss: 0.0028073659632354975\n",
+      "#600 Loss: 0.0026717206928879023\n",
+      "#700 Loss: 0.0025271554477512836\n",
+      "#800 Loss: 0.002374356146901846\n",
+      "#900 Loss: 0.0022152026649564505\n",
+      "Predicted data based on trained weights: \n",
+      "Input (scaled): \n",
+      "tensor([0.5000, 1.0000])\n",
+      "Output: \n",
+      "tensor([0.9335])\n",
+      "Finished training!\n"
+     ]
+    }
+   ],
+   "source": [
+    "NN = Neural_Network()\n",
+    "for i in range(1000):  # trains the NN 1,000 times\n",
+    "    if (i % 100) == 0:\n",
+    "        print (\"#\" + str(i) + \" Loss: \" + str(torch.mean((y - NN(X))**2).detach().item()))  # mean sum squared loss\n",
+    "    NN.train(X, y)\n",
+    "#NN.saveWeights(NN) # save weights\n",
+    "\n",
+    "NN.predict()\n",
+    "\n",
+    "print(\"Finished training!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The loss keeps decreasing, which means that the neural network is learning something. That's it. Congratulations! You have just learned how to create and train a neural network from scratch using PyTorch. There are so many things you can do with the shallow network we have just implemented. You can add more hidden layers or try to incorporate the bias terms for practice. I would love to see what you will build from here. Reach me out on [Twitter](https://twitter.com/omarsar0) if you have any further questions or leave your comments here. Until next time!\n",
+    "\n",
+    "## References:\n",
+    "- [PyTorch nn. Modules](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html#pytorch-custom-nn-modules)\n",
+    "- [Build a Neural Network with Numpy](https://enlight.nyc/neural-network)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('play')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "cf9800998463bc980d70cdbacff0c7e9a10687346dc898569e92f016d6e252c9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/pytorch_gentle_intro.ipynb b/notebooks/pytorch_gentle_intro.ipynb
new file mode 100644
index 0000000..f8262a9
--- /dev/null
+++ b/notebooks/pytorch_gentle_intro.ipynb
@@ -0,0 +1,755 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A Gentle Introduction to PyTorch\n",
+    "\n",
+    "In our previous PyTorch [notebook](https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d), we learned about how to get started quickly with PyTorch using Google Colab. In this tutorial, we are going to take a step back and review some of the basic components of building a deep learning model using PyTorch. \n",
+    "\n",
+    "This will be a brief tutorial and will avoid using jargon and overcomplicated code. That said, this is perhaps the most basic of models you can build with PyTorch. \n",
+    "\n",
+    "If fact, it is so basic that it's ideal for those starting to learn about PyTorch and deep learning. So if you have a friend or colleague that wants to jump in, I highly encourage you to refer them to this tutorial as a starting point. Let's get started!\n",
+    "\n",
+    "\n",
+    "**Author:** [Elvis Saravia](https://twitter.com/omarsar0)\n",
+    "\n",
+    "**Complete Code Walkthrough:** [Blog post](https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Started\n",
+    "\n",
+    "We need to import a few modules which will be useful to obtain the necessary functions that will help us to build our deep learning model. The main ones are `torch` and `torchvision`. They contain the majority of the functions that you need to get started with PyTorch. However, as this is a deep learning tutorial we will need `torch.nn`, `torch.nn.functional` and `torchvision.transforms` which all contain utility functions to build our model. We probably won't use all the modules listed below but they are the typical modules you will be importing when starting your deep learning projects.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## The usual imports\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "import torchvision.transforms as transforms\n",
+    "\n",
+    "## for printing image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the Data\n",
+    "Let's get right into it! As with any machine learning project, you need to load your dataset. We are using the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), which is the Hello World of datasets in the machine learning world. \n",
+    "\n",
+    "The data consists of number images that are of size `28 X 28`. We will discuss the images shortly, but our plan is to load data into batches of size `32`, similar to the figure below.\n",
+    "\n",
+    "\n",
+    "![alt text](https://drive.google.com/uc?export=view&id=19AC_WpscyXkrK_o4PaFFGpt_jG0aJm_f)\n",
+    "\n",
+    "\n",
+    "Here are the complete steps we are performing when importing our data:\n",
+    "- We will import and tranform the data into tensors using the `transforms` module\n",
+    "- We will use `DataLoader` to build convenient data loaders, which makes it easy to efficiently feed data in batches to deep learning models. We will get to the topic of batches in a bit but for now just think of them as subsets of your data. \n",
+    "- As hinted above, we will also create batches of the data by setting the `batch` parameter inside the data loader. Notice we use batches of `32` in this tutorial but you can change it to `64` if you like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2.0%"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100.0%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "102.8%\n",
+      "8.6%"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n",
+      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100.0%\n",
+      "112.7%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
+      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "## parameter denoting the batch size\n",
+    "BATCH_SIZE = 32\n",
+    "\n",
+    "## transformations\n",
+    "transform = transforms.Compose(\n",
+    "    [transforms.ToTensor()])\n",
+    "\n",
+    "## download and load training dataset\n",
+    "trainset = torchvision.datasets.MNIST(root='./data', train=True,\n",
+    "                                        download=True, transform=transform)\n",
+    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,\n",
+    "                                          shuffle=True, num_workers=2)\n",
+    "\n",
+    "## download and load testing dataset\n",
+    "testset = torchvision.datasets.MNIST(root='./data', train=False,\n",
+    "                                       download=True, transform=transform)\n",
+    "testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,\n",
+    "                                         shuffle=False, num_workers=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's inspect what the trainset and testset objects contain. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset MNIST\n",
+      "    Number of datapoints: 60000\n",
+      "    Root location: ./data\n",
+      "    Split: Train\n",
+      "    StandardTransform\n",
+      "Transform: Compose(\n",
+      "               ToTensor()\n",
+      "           )\n",
+      "Dataset MNIST\n",
+      "    Number of datapoints: 10000\n",
+      "    Root location: ./data\n",
+      "    Split: Test\n",
+      "    StandardTransform\n",
+      "Transform: Compose(\n",
+      "               ToTensor()\n",
+      "           )\n"
+     ]
+    }
+   ],
+   "source": [
+    "## print the trainset and testset\n",
+    "print(trainset)\n",
+    "print(testset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a beginner's tutorial so I will break down things a bit here:\n",
+    "- `BATCH_SIZE` is a parameter that denotes the batch size we will use for our model\n",
+    "- `transform` holds code for whatever transformations you will apply to your data. I will show you an example below to demonstrate exactly what it does to shed more light into its use\n",
+    "- `trainset` and `testset` contain the actual dataset object. Notice I use `train=True` to specify that this corresponds to the training dataset, and I use `train=False` to specify that this is the remainder of the dataset which we call the testset. From the portion I printed above you can see that the split of the data was 85% (60000) / 15% (10000), corresponding to the portions of samples for training set and testing set, respectively. \n",
+    "- `trainloader` is what holds the data loader object which takes care of shuffling the data and constructing the batches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at that `transforms.Compose(...)` function and see what it does. We will use a randomized image to demonstrate its use. Let's generate an image. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = transforms.ToPILImage(mode='L')(torch.randn(1, 96, 96))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**And** let's render it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7efc6a911f10>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, we have our image sample. And now let's apply some dummy transformation to it. We are going to rotate the image by `45` degrees. The transformation below takes care of that:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7efc683f7650>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## dummy transformation\n",
+    "dummy_transform = transforms.Compose(\n",
+    "    [transforms.RandomRotation(45)])\n",
+    "\n",
+    "dummy_result = dummy_transform(image)\n",
+    "\n",
+    "plt.imshow(dummy_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice you can put the transformations within `transforms.Compose(...)`. You can use the built in transformations offered by PyTorch or you can build your own and compose as you wish. In fact, you can place as many transformation as you wish in there. Let's try another composition of transformations: rotate + vertical flip. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7efc6837bd90>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## dummy transform \n",
+    "dummy2_transform = transforms.Compose(\n",
+    "    [transforms.RandomRotation(45), transforms.RandomVerticalFlip()])\n",
+    "\n",
+    "dummy2_result = dummy2_transform(image)\n",
+    "\n",
+    "plt.imshow(dummy2_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's pretty cool right! Keep trying other transform methods. On the topic of exploring our data further, let's take a look at our images dataset. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Data\n",
+    "As a practioner and researcher, I am always spend a bit of time and effort exploring and understanding my datasets. It's fun and this is a good practise to ensure that everything is in order.\n",
+    "\n",
+    "Let's check what the train and test dataset contain. I will use matplotlib to print out some of the images from our dataset. With a bit of numpy I can convert images into numpy and print them out. Below I print out an entire batch. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## functions to show an image\n",
+    "def imshow(img):\n",
+    "    #img = img / 2 + 0.5     # unnormalize\n",
+    "    npimg = img.numpy()\n",
+    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
+    "\n",
+    "## get some random training images\n",
+    "dataiter = iter(trainloader)\n",
+    "images, labels = dataiter.next()\n",
+    "\n",
+    "## show images\n",
+    "imshow(torchvision.utils.make_grid(images))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dimensions of our batches are as follow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image batch dimensions: torch.Size([32, 1, 28, 28])\n",
+      "Image label dimensions: torch.Size([32])\n"
+     ]
+    }
+   ],
+   "source": [
+    "for images, labels in trainloader:\n",
+    "    print(\"Image batch dimensions:\", images.shape)\n",
+    "    print(\"Image label dimensions:\", labels.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Model\n",
+    "Now it's time to build the deep learning model that will be used to perform the image classification. We will keeps things simple and stack a few dense layers and a dropout layer to train our model.\n",
+    "\n",
+    "Let's discuss a bit about the model:\n",
+    "\n",
+    "- First of all the following structure involving a `class` is standard code that's used to build the neural network model in PyTorch:\n",
+    "\n",
+    "```python\n",
+    "class MyModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(MyModel, self).__init__()\n",
+    "        \n",
+    "        # layers go here\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        \n",
+    "        # computations go here\n",
+    "```\n",
+    "- The layers are defined inside `def __init__()`. `super(...).__init__()` is just there to stick things together. For our model, we stack a hidden layer (`self.d1`) followed by a dropout layer (`self.dropout`), which is then followed by an output layer (`self.d2`). \n",
+    "- `nn.Linear(...)` defines the dense layer and it requires the `in` and `out` dimensions, which corresponds to the size of the input feature and output feature of that layer, respectively. \n",
+    "- `nn.Dropout(...)` is used to define a dropout layer. Dropout is an approach in deep learning that helps a model to avoid overfitting. This means that dropout acts as a regularization technique that helps the model to not overfit on the images it has seen while training. We want this because we need a model that generalizes well to unseen examples -- in our case, the testing dataset. Dropout randomly zeroes some of the units of the neural network layer with probability of `p=0.2`. Read more about the dropout layer [here](https://pytorch.org/docs/stable/nn.html#dropout). \n",
+    "- The entry point of the model, i.e. where the data enters, is placed under the `forward(...)` function. Typically, we also place other transformations we perform on the data while training inside this function. \n",
+    "- In the `forward()` function we are performing a series of computations on the input data\n",
+    "    - we flatten the images first, converting it from 2D (`28 X 28`) to 1D (`1 X 784`).\n",
+    "    - then we feed the batches of those 1D images into the first hidden layer\n",
+    "    - the output of that hidden layer is then applied a [non-linear activate function](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) called `ReLU`. It's not so important to know what `F.relu()` does at the moment, but the effect that it achieves is that it allows faster and more effective training of neural architectures on large datasets\n",
+    "    - as explained above, the dropout also helps the model to train more efficiently by avoiding overfitting on the training data\n",
+    "    - we then feed the output of that dropout layer into the output layer (`d2`)\n",
+    "    - the result of that is then fed to the [softmax function](https://en.wikipedia.org/wiki/Softmax_function), which converts or normalized the output into a probability distribution which helps with outputting proper predictions values that are used to calculate the accuracy of the model; this will the final output of the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## the model\n",
+    "class MyModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(MyModel, self).__init__()\n",
+    "        self.d1 = nn.Linear(28 * 28, 128)\n",
+    "        self.dropout = nn.Dropout(p=0.2)\n",
+    "        self.d2 = nn.Linear(128, 10)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = x.flatten(start_dim = 1)\n",
+    "        x = self.d1(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.dropout(x)\n",
+    "        logits = self.d2(x)\n",
+    "        out = F.softmax(logits, dim=1)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visually, the following is a diagram of the model we have built. Just keep in mind that the hidden layer is much bigger as shown in the diagram but due to space constraint, the diagram is just an approximation to the actual model. \n",
+    "\n",
+    "![alt text](https://drive.google.com/uc?export=view&id=1NuFflDPOW_hIAHTH2pXZAEhSINygPlnB)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As I have done in my previous tutorials, I always encourage to test the model with 1 batch to ensure that the output dimensions are what we expect. Notice how we are iterating over the dataloader which conveniently stores the `images` and `labels` pairs. `out` contains the output of the model, which are the logits applied a `softmax` layer which helps with prediction. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "batch size: torch.Size([32, 1, 28, 28])\n",
+      "torch.Size([32, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "## test the model with 1 batch\n",
+    "model = MyModel()\n",
+    "for images, labels in trainloader:\n",
+    "    print(\"batch size:\", images.shape)\n",
+    "    out = model(images)\n",
+    "    print(out.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can clearly see that we get back the batches with 10 output values associate with it. These are used to compute the performance of the model. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "Now we are ready to train the model but before that we are going to setup a loss function, an optimizer and a function to compute accuracy of the model. \n",
+    "\n",
+    "- The `learning_rate` is the rate at which the model will try to optimize its weights, which is just another parameter for the model. \n",
+    "- `num_epochs` is the number of training steps. \n",
+    "- `device` determines what hardware we will use to train the model. If a `gpu` is present, then that will be used, otherwise it defaults to the `cpu`.\n",
+    "- `model` is just the model instance.\n",
+    "- `model.to(device)` is in charge of setting the actaull device that will be used for training the model\n",
+    "- `criterion` is just the metric that's used to compute the loss of the model while it forward and backward trains to optimize its weights. \n",
+    "- `optimizer` is the optimization technique used to modify the weights in the backward propagation. Notice that it requires the `learning_rate` and the model parameters which are part of the calculation to optimize weights.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learning_rate = 0.001\n",
+    "num_epochs = 5\n",
+    "\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model = MyModel()\n",
+    "model = model.to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The utility function below helps to calculate the accuracy of the model. For now, it's not important to understand how it's calculated but basically it compares the outputs of the model (predictions) with the actual target values (i.e., the labels of the dataset), and tries to compute the average of correct predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## utility function to compute accuracy\n",
+    "def get_accuracy(output, target, batch_size):\n",
+    "    ''' Obtain accuracy for training round '''\n",
+    "    corrects = (torch.max(output, 1)[1].view(target.size()).data == target.data).sum()\n",
+    "    accuracy = 100.0 * corrects/batch_size\n",
+    "    return accuracy.item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "Now it's time to train the model. The code portion that follows can be descrive in the following steps:\n",
+    "\n",
+    "- The first thing in training a neural network model is defining the training loop, which is achieved by:\n",
+    "\n",
+    "```python\n",
+    "for epoch in range(num_epochs):\n",
+    "    ...\n",
+    "```\n",
+    "\n",
+    "- We define two variables, `training_running_loss` and `train_acc` that will help us to monitor the running accuracy and loss of the modes while it trains over the different batches.\n",
+    "- `model.train()` explicitly indicates that we are ready to start training. \n",
+    "- Notice how we are iterating over the dataloader, which conveniently gives us the batches in image-label pairs. \n",
+    "- That second `for` loop means that for every training step we will iterate over all the batches and train the model over them.\n",
+    "- We feed the model the images via `model(images)` and the output are the predictions of the model. \n",
+    "- The predictions together with the target labels are used to compute the loss using the loss function we defined earlier.\n",
+    "- Before we update our weights for the next round of training, we perform the following steps:\n",
+    "    - we use the optimizer object to reset all the gradients for the variables it will update. This is a safe step and it doesn't overwrites the gradients the model accumulates while training (those are stored in a buffer [link text](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html#pytorch-optim) via the `loss.backward() call)\n",
+    "    - `loss.backward()` simply computes the gradient of the loss w.r.t to the model parameters\n",
+    "    - `optimizer.step()` then ensures that the model parameters are updated\n",
+    "\n",
+    "- Then we gather and accumulate the loss and accuracy, which is what we will use to tell us if the model is learning properly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 | Loss: 1.5929 | Train Accuracy: 89.11\n",
+      "Epoch: 1 | Loss: 1.5275 | Train Accuracy: 94.13\n",
+      "Epoch: 2 | Loss: 1.5138 | Train Accuracy: 95.33\n",
+      "Epoch: 3 | Loss: 1.5063 | Train Accuracy: 95.99\n",
+      "Epoch: 4 | Loss: 1.5003 | Train Accuracy: 96.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "## train the model\n",
+    "for epoch in range(num_epochs):\n",
+    "    train_running_loss = 0.0\n",
+    "    train_acc = 0.0\n",
+    "\n",
+    "    ## commence training\n",
+    "    model = model.train()\n",
+    "\n",
+    "    ## training step\n",
+    "    for i, (images, labels) in enumerate(trainloader):\n",
+    "        \n",
+    "        images = images.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        ## forward + backprop + loss\n",
+    "        predictions = model(images)\n",
+    "        loss = criterion(predictions, labels)\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "\n",
+    "        ## update model params\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        train_running_loss += loss.detach().item()\n",
+    "        train_acc += get_accuracy(predictions, labels, BATCH_SIZE)\n",
+    "    \n",
+    "    model.eval()\n",
+    "    print('Epoch: %d | Loss: %.4f | Train Accuracy: %.2f' \\\n",
+    "          %(epoch, train_running_loss / i, train_acc/i)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After all the training steps are over, we can clearly see that the loss keeps decreasing while the training accuracy of the model keeps rising, which is a good sign that the model is effectively learning to classify images.\n",
+    "\n",
+    "We can verify that by computing the accuracy on the testing dataset to see how well the model performs on the image classificaiton task. As you can see below, our basic CNN model is performing very well on the MNIST classification task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy: 96.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_acc = 0.0\n",
+    "for i, (images, labels) in enumerate(testloader, 0):\n",
+    "    images = images.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "    outputs = model(images)\n",
+    "    test_acc += get_accuracy(outputs, labels, BATCH_SIZE)\n",
+    "        \n",
+    "print('Test Accuracy: %.2f'%( test_acc/i))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final Words\n",
+    "\n",
+    "Congratulation! You have made it to the end of this tutorial. This is a really long tutorial that aims to give an very basic introduction to the fundamentals of image classification using neural networks and PyTorch.\n",
+    "\n",
+    "*This tutorial was heavily inspired by this [TensorFlow tutorial.](https://www.tensorflow.org/beta/tutorials/quickstart/beginner) We thank the authors of the corresponding reference for their valuable work.*\n",
+    "\n",
+    "## References\n",
+    "- [PyTorch 1.2 Quickstart with Google Colab](https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d)\n",
+    "- [Get started with TensorFlow 2.0 for beginners](https://www.tensorflow.org/beta/tutorials/quickstart/beginner)\n",
+    "- [PyTorch Data Loading Tutorial](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html)\n",
+    "-[ Neural Networks with PyTorch](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('nlp')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "154abf72fb8cc0db1aa0e7366557ff891bff86d6d75b7e5f2e68a066d591bfd7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/pytorch_hello_world.ipynb b/notebooks/pytorch_hello_world.ipynb
new file mode 100644
index 0000000..abfec51
--- /dev/null
+++ b/notebooks/pytorch_hello_world.ipynb
@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A First Shot at Deep Learning with PyTorch\n",
+    "\n",
+    "In this notebook, we are going to take a baby step into the world of deep learning using PyTorch. There are a ton of notebooks out there that teach you the fundamentals of deep learning and PyTorch, so here the idea is to give you some basic introduction to deep learning and PyTorch at a very high level. Therefore, this notebook is targeting beginners but it can also serve as a review for more experienced developers.\n",
+    "\n",
+    "After completion of this notebook, you are expected to know the basic components of training a basic neural network with PyTorch. I have also left a couple of exercises towards the end with the intention of encouraging more research and practise of your deep learning skills. \n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Author:** Elvis Saravia([Twitter](https://twitter.com/omarsar0) | [LinkedIn](https://www.linkedin.com/in/omarsar/))\n",
+    "\n",
+    "**Complete Code Walkthrough:** [Blog post](https://medium.com/dair-ai/a-first-shot-at-deep-learning-with-pytorch-4a8252d30c75)\n",
+    "\n",
+    "## Importing the libraries\n",
+    "\n",
+    "Like with any other programming exercise, the first step is to import the necessary libraries. As we are going to be using Google Colab to program our neural network, we need to install and import the necessary PyTorch libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.11.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## The usual imports\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "## print out the pytorch version used\n",
+    "print(torch.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Neural Network\n",
+    "\n",
+    "![alt text](https://drive.google.com/uc?export=view&id=1Lpi4VPBfAV3JkOLopcsGK4L8dyxmPF1b)\n",
+    "\n",
+    "Before building and training a neural network the first step is to process and prepare the data. In this notebook, we are going to use syntethic data (i.e., fake data) so we won't be using any real world data. \n",
+    "\n",
+    "For the sake of simplicity, we are going to use the following input and output pairs converted to tensors, which is how data is typically represented in the world of deep learning. The x values represent the input of dimension `(6,1)` and the y values represent the output of similar dimension. The example is taken from this [tutorial](https://github.com/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%202%20-%20Lesson%202%20-%20Notebook.ipynb). \n",
+    "\n",
+    "The objective of the neural network model that we are going to build and train is to automatically learn patterns that better characterize the relationship between the `x` and `y` values. Essentially, the model learns the relationship that exists between inputs and outputs which can then be used to predict the corresponding `y` value for any given input `x`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## our data in tensor form\n",
+    "x = torch.tensor([[-1.0],  [0.0], [1.0], [2.0], [3.0], [4.0]], dtype=torch.float)\n",
+    "y = torch.tensor([[-3.0], [-1.0], [1.0], [3.0], [5.0], [7.0]], dtype=torch.float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([6, 1])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## print size of the input tensor\n",
+    "x.size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Neural Network Components\n",
+    "As said earlier, we are going to first define and build out the components of our neural network before training the model.\n",
+    "\n",
+    "### Model\n",
+    "\n",
+    "Typically, when building a neural network model, we define the layers and weights which form the basic components of the model. Below we show an example of how to define a hidden layer named `layer1` with size `(1, 1)`. For the purpose of this tutorial, we won't explicitly define the `weights` and allow the built-in functions provided by PyTorch to handle that part for us. By the way, the `nn.Linear(...)` function applies a linear transformation ($y = xA^T + b$) to the data that was provided as its input. We ignore the bias for now by setting `bias=False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Neural network with 1 hidden layer\n",
+    "layer1 = nn.Linear(1,1, bias=False)\n",
+    "model = nn.Sequential(layer1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loss and Optimizer\n",
+    "The loss function, `nn.MSELoss()`, is in charge of letting the model know how good it has learned the relationship between the input and output. The optimizer (in this case an `SGD`) primary role is to minimize or lower that loss value as it tunes its weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## loss function\n",
+    "criterion = nn.MSELoss()\n",
+    "\n",
+    "## optimizer algorithm\n",
+    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Neural Network Model\n",
+    "We have all the components we need to train our model. Below is the code used to train our model. \n",
+    "\n",
+    "In simple terms, we train the model by feeding it the input and output pairs for a couple of rounds (i.e., `epoch`). After a series of forward and backward steps, the model somewhat learns the relationship between x and y values. This is notable by the decrease in the computed `loss`. For a more detailed explanation of this code check out this [tutorial](https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 | Loss: 38.4262\n",
+      "Epoch: 1 | Loss: 31.0058\n",
+      "Epoch: 2 | Loss: 25.0396\n",
+      "Epoch: 3 | Loss: 20.2428\n",
+      "Epoch: 4 | Loss: 16.3860\n",
+      "Epoch: 5 | Loss: 13.2852\n",
+      "Epoch: 6 | Loss: 10.7921\n",
+      "Epoch: 7 | Loss: 8.7876\n",
+      "Epoch: 8 | Loss: 7.1760\n",
+      "Epoch: 9 | Loss: 5.8802\n",
+      "Epoch: 10 | Loss: 4.8384\n",
+      "Epoch: 11 | Loss: 4.0008\n",
+      "Epoch: 12 | Loss: 3.3273\n",
+      "Epoch: 13 | Loss: 2.7858\n",
+      "Epoch: 14 | Loss: 2.3505\n",
+      "Epoch: 15 | Loss: 2.0004\n",
+      "Epoch: 16 | Loss: 1.7190\n",
+      "Epoch: 17 | Loss: 1.4927\n",
+      "Epoch: 18 | Loss: 1.3108\n",
+      "Epoch: 19 | Loss: 1.1646\n",
+      "Epoch: 20 | Loss: 1.0470\n",
+      "Epoch: 21 | Loss: 0.9524\n",
+      "Epoch: 22 | Loss: 0.8764\n",
+      "Epoch: 23 | Loss: 0.8153\n",
+      "Epoch: 24 | Loss: 0.7661\n",
+      "Epoch: 25 | Loss: 0.7266\n",
+      "Epoch: 26 | Loss: 0.6948\n",
+      "Epoch: 27 | Loss: 0.6693\n",
+      "Epoch: 28 | Loss: 0.6488\n",
+      "Epoch: 29 | Loss: 0.6322\n",
+      "Epoch: 30 | Loss: 0.6190\n",
+      "Epoch: 31 | Loss: 0.6083\n",
+      "Epoch: 32 | Loss: 0.5997\n",
+      "Epoch: 33 | Loss: 0.5928\n",
+      "Epoch: 34 | Loss: 0.5873\n",
+      "Epoch: 35 | Loss: 0.5828\n",
+      "Epoch: 36 | Loss: 0.5792\n",
+      "Epoch: 37 | Loss: 0.5763\n",
+      "Epoch: 38 | Loss: 0.5740\n",
+      "Epoch: 39 | Loss: 0.5722\n",
+      "Epoch: 40 | Loss: 0.5707\n",
+      "Epoch: 41 | Loss: 0.5695\n",
+      "Epoch: 42 | Loss: 0.5685\n",
+      "Epoch: 43 | Loss: 0.5677\n",
+      "Epoch: 44 | Loss: 0.5671\n",
+      "Epoch: 45 | Loss: 0.5666\n",
+      "Epoch: 46 | Loss: 0.5662\n",
+      "Epoch: 47 | Loss: 0.5659\n",
+      "Epoch: 48 | Loss: 0.5656\n",
+      "Epoch: 49 | Loss: 0.5654\n",
+      "Epoch: 50 | Loss: 0.5652\n",
+      "Epoch: 51 | Loss: 0.5651\n",
+      "Epoch: 52 | Loss: 0.5650\n",
+      "Epoch: 53 | Loss: 0.5649\n",
+      "Epoch: 54 | Loss: 0.5648\n",
+      "Epoch: 55 | Loss: 0.5647\n",
+      "Epoch: 56 | Loss: 0.5647\n",
+      "Epoch: 57 | Loss: 0.5647\n",
+      "Epoch: 58 | Loss: 0.5646\n",
+      "Epoch: 59 | Loss: 0.5646\n",
+      "Epoch: 60 | Loss: 0.5646\n",
+      "Epoch: 61 | Loss: 0.5646\n",
+      "Epoch: 62 | Loss: 0.5646\n",
+      "Epoch: 63 | Loss: 0.5646\n",
+      "Epoch: 64 | Loss: 0.5645\n",
+      "Epoch: 65 | Loss: 0.5645\n",
+      "Epoch: 66 | Loss: 0.5645\n",
+      "Epoch: 67 | Loss: 0.5645\n",
+      "Epoch: 68 | Loss: 0.5645\n",
+      "Epoch: 69 | Loss: 0.5645\n",
+      "Epoch: 70 | Loss: 0.5645\n",
+      "Epoch: 71 | Loss: 0.5645\n",
+      "Epoch: 72 | Loss: 0.5645\n",
+      "Epoch: 73 | Loss: 0.5645\n",
+      "Epoch: 74 | Loss: 0.5645\n",
+      "Epoch: 75 | Loss: 0.5645\n",
+      "Epoch: 76 | Loss: 0.5645\n",
+      "Epoch: 77 | Loss: 0.5645\n",
+      "Epoch: 78 | Loss: 0.5645\n",
+      "Epoch: 79 | Loss: 0.5645\n",
+      "Epoch: 80 | Loss: 0.5645\n",
+      "Epoch: 81 | Loss: 0.5645\n",
+      "Epoch: 82 | Loss: 0.5645\n",
+      "Epoch: 83 | Loss: 0.5645\n",
+      "Epoch: 84 | Loss: 0.5645\n",
+      "Epoch: 85 | Loss: 0.5645\n",
+      "Epoch: 86 | Loss: 0.5645\n",
+      "Epoch: 87 | Loss: 0.5645\n",
+      "Epoch: 88 | Loss: 0.5645\n",
+      "Epoch: 89 | Loss: 0.5645\n",
+      "Epoch: 90 | Loss: 0.5645\n",
+      "Epoch: 91 | Loss: 0.5645\n",
+      "Epoch: 92 | Loss: 0.5645\n",
+      "Epoch: 93 | Loss: 0.5645\n",
+      "Epoch: 94 | Loss: 0.5645\n",
+      "Epoch: 95 | Loss: 0.5645\n",
+      "Epoch: 96 | Loss: 0.5645\n",
+      "Epoch: 97 | Loss: 0.5645\n",
+      "Epoch: 98 | Loss: 0.5645\n",
+      "Epoch: 99 | Loss: 0.5645\n",
+      "Epoch: 100 | Loss: 0.5645\n",
+      "Epoch: 101 | Loss: 0.5645\n",
+      "Epoch: 102 | Loss: 0.5645\n",
+      "Epoch: 103 | Loss: 0.5645\n",
+      "Epoch: 104 | Loss: 0.5645\n",
+      "Epoch: 105 | Loss: 0.5645\n",
+      "Epoch: 106 | Loss: 0.5645\n",
+      "Epoch: 107 | Loss: 0.5645\n",
+      "Epoch: 108 | Loss: 0.5645\n",
+      "Epoch: 109 | Loss: 0.5645\n",
+      "Epoch: 110 | Loss: 0.5645\n",
+      "Epoch: 111 | Loss: 0.5645\n",
+      "Epoch: 112 | Loss: 0.5645\n",
+      "Epoch: 113 | Loss: 0.5645\n",
+      "Epoch: 114 | Loss: 0.5645\n",
+      "Epoch: 115 | Loss: 0.5645\n",
+      "Epoch: 116 | Loss: 0.5645\n",
+      "Epoch: 117 | Loss: 0.5645\n",
+      "Epoch: 118 | Loss: 0.5645\n",
+      "Epoch: 119 | Loss: 0.5645\n",
+      "Epoch: 120 | Loss: 0.5645\n",
+      "Epoch: 121 | Loss: 0.5645\n",
+      "Epoch: 122 | Loss: 0.5645\n",
+      "Epoch: 123 | Loss: 0.5645\n",
+      "Epoch: 124 | Loss: 0.5645\n",
+      "Epoch: 125 | Loss: 0.5645\n",
+      "Epoch: 126 | Loss: 0.5645\n",
+      "Epoch: 127 | Loss: 0.5645\n",
+      "Epoch: 128 | Loss: 0.5645\n",
+      "Epoch: 129 | Loss: 0.5645\n",
+      "Epoch: 130 | Loss: 0.5645\n",
+      "Epoch: 131 | Loss: 0.5645\n",
+      "Epoch: 132 | Loss: 0.5645\n",
+      "Epoch: 133 | Loss: 0.5645\n",
+      "Epoch: 134 | Loss: 0.5645\n",
+      "Epoch: 135 | Loss: 0.5645\n",
+      "Epoch: 136 | Loss: 0.5645\n",
+      "Epoch: 137 | Loss: 0.5645\n",
+      "Epoch: 138 | Loss: 0.5645\n",
+      "Epoch: 139 | Loss: 0.5645\n",
+      "Epoch: 140 | Loss: 0.5645\n",
+      "Epoch: 141 | Loss: 0.5645\n",
+      "Epoch: 142 | Loss: 0.5645\n",
+      "Epoch: 143 | Loss: 0.5645\n",
+      "Epoch: 144 | Loss: 0.5645\n",
+      "Epoch: 145 | Loss: 0.5645\n",
+      "Epoch: 146 | Loss: 0.5645\n",
+      "Epoch: 147 | Loss: 0.5645\n",
+      "Epoch: 148 | Loss: 0.5645\n",
+      "Epoch: 149 | Loss: 0.5645\n"
+     ]
+    }
+   ],
+   "source": [
+    "## training\n",
+    "for ITER in range(150):\n",
+    "    model = model.train()\n",
+    "\n",
+    "    ## forward\n",
+    "    output = model(x)\n",
+    "    loss = criterion(output, y)\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    ## backward + update model params \n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    model.eval()\n",
+    "    print('Epoch: %d | Loss: %.4f' %(ITER, loss.detach().item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing the Model\n",
+    "After training the model we have the ability to test the model predictive capability by passing it an input. Below is a simple example of how you could achieve this with our model. The result we obtained aligns with the results obtained in this [notebook](https://github.com/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%202%20-%20Lesson%202%20-%20Notebook.ipynb), which inspired this entire tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17.096769332885742\n"
+     ]
+    }
+   ],
+   "source": [
+    "## test the model\n",
+    "sample = torch.tensor([10.0], dtype=torch.float)\n",
+    "predicted = model(sample)\n",
+    "print(predicted.detach().item())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final Words\n",
+    "\n",
+    "Congratulations! In this tutorial you learned how to train a simple neural network using PyTorch. You also learned about the basic components that make up a neural network model such as the linear transformation layer, optimizer, and loss function. We then trained the model and tested its predictive capabilities. You are well on your way to become more knowledgeable about deep learning and PyTorch. I have provided a bunch of references below if you are interested in practising and learning more. \n",
+    "\n",
+    "*I would like to thank Laurence Moroney for his excellent [tutorial](https://github.com/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%202%20-%20Lesson%202%20-%20Notebook.ipynb) which I used as an inspiration for this tutorial.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "- Add more examples in the input and output tensors. In addition, try to change the dimensions of the data, say by adding an extra value in each array. What needs to be changed to successfully train the network with the new data?\n",
+    "- The model converged really fast, which means it learned the relationship between x and y values after a couple of iterations. Do you think it makes sense to continue training? How would you automate the process of stopping the training after the model loss doesn't subtantially change?\n",
+    "- In our example, we used a single hidden layer. Try to take a look at the PyTorch documentation to figure out what you need to do to get a model with more layers. What happens if you add more hidden layers?\n",
+    "- We did not discuss the learning rate (`lr-0.001`) and the optimizer in great detail. Check out the [PyTorch documentation](https://pytorch.org/docs/stable/optim.html) to learn more about what other optimizers you can use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "- [The Hello World of Deep Learning with Neural Networks](https://github.com/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%202%20-%20Lesson%202%20-%20Notebook.ipynb)\n",
+    "- [A Simple Neural Network from Scratch with PyTorch and Google Colab](https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0?source=collection_category---4------1-----------------------)\n",
+    "- [PyTorch Official Docs](https://pytorch.org/docs/stable/nn.html)\n",
+    "- [PyTorch 1.2 Quickstart with Google Colab](https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d)\n",
+    "- [A Gentle Intoduction to PyTorch](https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('nlp')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "7ade285f687a1ecab6f569c64721a8142b161535723b6a0ecd56d473b77660bf"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/spec-file.txt b/spec-file.txt
new file mode 100644
index 0000000..5df10d3
--- /dev/null
+++ b/spec-file.txt
@@ -0,0 +1,101 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+@EXPLICIT
+https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda
+https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2022.07.19-h06a4308_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2021.4.0-h06a4308_3561.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libgfortran4-7.5.0-ha8ba4b0_17.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda
+https://conda.anaconda.org/pytorch/noarch/pytorch-mutex-1.0-cuda.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.5.0-ha8ba4b0_17.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/mkl-2021.4.0-h06a4308_640.conda
+https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/cudatoolkit-11.3.1-h2bc3f7f_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/giflib-5.2.1-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gmp-6.2.1-h295c915_3.conda
+https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h7f8727e_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/lame-3.100-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.3-he6710b0_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libopus-1.3.1-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libsodium-1.0.18-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libtasn1-4.16.0-h27cfd23_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libunistring-0.9.10-h27cfd23_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libuv-1.40.0-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libvpx-1.7.0-h439df22_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.2.2-h7f8727e_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.3-h295c915_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.3-h7f8727e_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/openh264-2.1.1-h4ff587b_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/openssl-1.1.1q-h7f8727e_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/x264-1!157.20191217-h7b6447c_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/xz-5.2.5-h7f8727e_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.12-h7f8727e_2.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libidn2-2.3.2-h7f8727e_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.37-hbc83047_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/nettle-3.7.3-hbbd107a_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/readline-8.1.2-h7f8727e_1.conda
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+https://repo.anaconda.com/pkgs/main/linux-64/zeromq-4.3.4-h2531618_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.2-ha4553b6_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/freetype-2.11.0-h70c0345_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/gnutls-3.6.15-he1e5248_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.2.0-h2818925_1.conda
+https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.38.3-hc218d9a_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/ffmpeg-4.2.2-h20bf706_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/libwebp-1.2.2-h55f646e_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.7.13-h12debd9_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/backcall-0.2.0-pyhd3eb1b0_0.tar.bz2
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+https://repo.anaconda.com/pkgs/main/noarch/pickleshare-0.7.5-pyhd3eb1b0_1003.conda
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+https://conda.anaconda.org/pytorch/linux-64/torchaudio-0.11.0-py37_cu113.tar.bz2
+https://conda.anaconda.org/pytorch/linux-64/torchvision-0.12.0-py37_cu113.tar.bz2