Getting started with CNN building blocks

Although regular hidden layers (the fully connected layers we have seen so far) do a good job of extracting features from data at certain levels, these representations might not be useful in differentiating images of different classes. CNNs can be used to extract richer, more distinguishable representations that, for example, make a car a car, a plane a plane, or the handwritten letters “y” and “z” recognizably a “y” and a “z,” and so on. CNNs are a type of neural network that is biologically inspired by the human visual cortex. To demystify CNNs, I will start by introducing the components of a typical CNN, including the convolutional layer, the non-linear layer, and the pooling layer.

The convolutional layer

The convolutional layer is the first layer in a CNN, or the first few layers in a CNN if it has multiple convolutional layers.

CNNs, specifically their convolutional layers...