revising the structure of the repository

Author: lmassaron

.gitignore

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+.DS_Store
+.ipynb_checkpoints/
+logs/

1. Getting Started with TensorFlow 2.x.ipynb renamed to ch1/1. Getting Started with TensorFlow 2.x.ipynb

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+## Introduction to Convolutional Neural Networks
+
+Convolutional Neural Networks (CNNs) are responsible for the latest major breakthroughs in image recognition in the past few years.
+
+In mathematics, a convolution is a function which is applied over the output of another function.  In our case, we will consider applying a matrix mutliplication (filter) across an image. See the below diagram for an example of how this may work.
+
+![Convolutional Filter](../images/01_intro_cnn.png)
+
+CNNs generally follow a structure.  The main convolutional setup is (input array) -> (convolutional filter layer) -> (Pooling) -> (Activation layer).  The above diagram depicts how a convolutional layer may create one feature.  Generally, filters are multidimensional and end up creating many features.  It is also common to have a completely separate filter-feature creator of different sizes acting on the same layer.  After this convolutional filter, it is common to apply a pooling layer. This pooling may be a max-pooling or an average pooling or another aggregation. One of the key concepts here is that the pooling layer has no parameters- while decreasing the layer size.  See the below diagram for an example of max-pooling.
+
+![Convolutional Filter](../images/01_intro_cnn2.png)
+
+After the max pooling, there is generally an activation layer.  One of the more common activation layers is the ReLU (Rectified Linear Unit).  See [Chapter 1, Section 6](../../01_Introduction/06_Implementing_Activation_Functions) for examples.