Summary

This chapter mainly focused on ANNs (the MLP, in particular), which have become increasingly important in the field of machine learning due to their ability to tackle highly complex data problems that usually use extremely large datasets with patterns that are impossible to see with the human eye.

The main objective is to emulate the architecture of the human brain by using mathematical functions to process data. The process that is used to train an ANN consists of a forward propagation step, the calculation of a cost function, a backpropagation step, and the updating of the different weights and biases that help to map the input values to an output.

In addition to the variables of the weights and biases, ANNs have multiple hyperparameters that can be tuned to improve the performance of the network, which can be done by modifying the architecture or training process of the algorithm. Some of the most popular hyperparameters are the size of the network (in terms of hidden...