CNN Layer Composition — A Practical Developer Guide to Activation, Pooling, and Fully Connected Layers

This practical guide details the composition of CNN layers, explaining how activation, pooling, and fully connected layers work together to transform feature maps into predictions. It emphasizes the crucial role of non-linearity, introduced by functions like ReLU, for learning complex features and the network's effective operation.