Multilayer neural networks are models that chain many neurons in order to create a neural architecture. Individually, neurons are very basic units, but when organized together, we can create a model significantly more powerful than the individual neurons.
As touched upon in the previous section, we build neural networks in layers and we distinguish between different kinds of neural networks primarily on the basis of the connections that exist between these layers and the types of neurons used. The following diagram shows the general structure of a multilayer perceptron (MLP) neural network, shown here for two hidden layers:
The first characteristic of the MLP network is that the information flows in a single direction from input layer to output layer. Thus, it is known as a feedforward neural network. This is in contrast to other neural network types, in which there are cycles that allow information to flow back to earlier neurons in the network as a feedback...
