Summary

In this chapter, we saw neural networks as a nonlinear method capable of solving both regression and classification problems. Motivated by the biological analogy to human neurons, we first introduced the simplest neural network, the perceptron. This is able to solve binary classification problems only when the two classes are linearly separable, something that we very rarely rely upon in practice.

By changing the function that transforms the linear weighted combination of inputs, namely the activation function, we discovered how to create different types of individual neurons. A linear activation function creates a neuron that performs linear regression, whereas the logistic activation function creates a neuron that performs logistic regression. By organizing and connecting neurons into layers, we can create multilayer neural networks that are powerful models for solving nonlinear problems.

The idea behind having hidden layers of neurons is that each hidden layer learns a new set of...