Summary

The theory that gave birth to neural networks was developed decades ago by Frank Rosenblatt. It started with the definition of the perceptron, a unit inspired by the human neuron, that takes data as input to perform a transformation on it. The theory behind the perceptron consisted of assigning weights to input data to perform a calculation so that the end result would be either one thing or the other, depending on the outcome.

The most widely known form of neural networks is the one that's created from a succession of perceptrons, stacked together in layers, where the output from one column of perceptrons (layer) is the input for the following one.

The typical learning process for a neural network was explained. Here, there are three main processes to consider: forward propagation, the calculation of the loss function, and backpropagation.

The end goal of this procedure is to minimize the loss function by updating the weights and biases that accompany each of the input values in every neuron of the network. This is achieved through an iterative process that can take minutes, hours, or even weeks, depending on the nature of the data problem.

The main architecture of the three main types of neural networks was also discussed: the artificial neural network, the convolutional neural network, and the recurrent neural network. The first is used to solve traditional classification or regression problems, the second one is widely popular for its capacity to solve computer vision problems (for instance, image classification), and the third one is capable of processing data in sequence, which is useful for tasks such as language translation.

In the next chapter, the main differences between solving regression and a classification data problem will be discussed. You will also learn how to solve a classification data problem, as well as how to improve its performance and how to deploy the model.