As we can see, one simple example in which the patterns are not linearly separable has led us to more and more issue using the perceptron architecture. That need led to the application of multilayer perceptrons. In Chapter 1, Getting Started with Neural Networks we dealt with the fact that the natural neural network is structured in layers as well, and each layer captures pieces of information from a specific environment. In artificial neural networks, layers of neurons act in this way, by extracting and abstracting information from data, transforming them into another dimension or shape.
In the XOR example, we found the solution to be the addition of a third component that would make possible a linear separation. But there remained a few questions regarding how that third component would be computed. Now let's consider the same solution as a two-layer perceptron:
Now we have three neurons instead of just one, but in the output the information transferred by the previous...