# Summary

In this chapter, we started our exploration of the deep learning world by introducing the basic concepts that led the first researchers to improve algorithms until they achieved the top results we can achieve nowadays. The first part explained the structure of a basic artificial neuron, which combines a linear operation followed by an optional non-linear scalar function. A single layer of linear neurons was initially proposed as the first neural network, with the name of the perceptron.

Even though it was quite powerful for many problems, this model soon showed its limitations when working with non-linear separable datasets. A perceptron is not very different from logistic regression, and there's no concrete reason to employ it. Nevertheless, this model opened the doors to a family of extremely powerful models obtained by combining multiple non-linear layers. The multilayer perceptron, which has been proven to be a universal approximator, is able to manage almost any...