Now that we know all the different components that make up a ConvNet, we can put it all together and see how to construct a deep CNN. In this section, we will build a full architecture and observe how forward propagation works and how we decide the depth of the network, the number of kernels to apply, when and why to use pooling, and so on. But before we dive in, let's explore some of the ways in which CNNs differ from FNNs. They are as follows:
- The neurons in CNNs have local connectivity, which means that each neuron in a successive layer receives input from a small local group of pixels from an image, instead of receiving the entire image, as a feedforward neural network (FNN) would.
- Each neuron in the layer of a CNN has the same weight parameters.
- The layers in CNNs can be normalized.
- CNNs are translation invariant, which allows us...
