Multilayer perceptron — the first example of a network

In this chapter, we define the first example of a network with multiple linear layers. Historically, perceptron was the name given to a model having one single linear layer, and as a consequence, if it has multiple layers, you would call it multilayer perceptron (MLP). The following image represents a generic neural network with one input layer, one intermediate layer and one output layer.

In the preceding diagram, each node in the first layer receives an input and fires according to the predefined local decision boundaries. Then the output of the first layer is passed to the second layer, the results of which are passed to the final output layer consisting of one single neuron. It is interesting to note that this layered organization vaguely resembles the patterns of human vision we discussed earlier.

The net is dense, meaning that each neuron in a layer is connected to all neurons located in the previous layer and to all the neurons in the following layer.