A book by Ian Pointer

This Repository contain codes from the book Programming PyTorch for Deep Learning : Creating and Deploying Deep Learning Applications

To begin, you'll need to clone this repository on your local machine. You can do this by running the following command in your terminal:

git clone https://github.com/rsanimesh/Programming-PyTorch-for-Deep-Learning.git

Make sure you have Python installed on your machine. If not, you can download it from the official Python website: Python Downloads. During installation, be sure to check the "Add Python to PATH" option.

It's a good practise to work in a virtual environment to manage project dependencies. Open your command prompt or terminal and navigate to the cloned repository. Then, proceed to create and activate a virtual environment by following these steps:

For Windows: Using PIP

pip install virtualenv
python -m venv venv
venv\Scripts\activate

For macOS and Linux: Using PIP

pip install virtualenv
python3 -m venv venv
source venv/bin/activate

In the root directory of the repository, you'll find requirement files that contain a list of available libraries along with their compatible versions. If you have CUDA installed on your system, then use the file requirements_cuda_available.txt or requirements.txt. Install the required libraries by running:

pip install -r requirements_cuda_available.txt or pip install -r requirements.txt

This will set up the necessary dependencies to run the code examples.

To work with the code and Jupyter Notebook, simply run the following command: jupyter notebook

• Building an image classifier.ipynb

• Building a CNN model.ipynb

• Transfer learning with ResNet.ipynb
• Finding that learning rate.ipynb