An introduction

What we did in the previous exercise was nothing more than training a network of neurons in our brain giving it inputs (force, angle and direction in which we threw the paper) and the result obtained with each shot was an exit from said network that helped us to train it. Let's start dismembering this.

The primary component for training this network of neurons is error. It is what gives us the necessary information to train the network and thus obtain the expected result. When the first paper was 1 meter away from the basket, the error was 1 meter, so we trained a network based on the results obtained, to then change the value of each neuron that is part of the network, and obtain the desired output (to make the paper land in the basket). We can say that once we train the neuron network, we have already learned to throw the papers and put them in the basket. Learning that is saved and we can then take advantage of again.

The same thing happens in artificial neural networks: for example, we can train a network to teach it to add 2 whole numbers and obtain the result of said sum at the output.

An artificial neuron (also called a perceptron) is the atomic unit of a neural network and does not represent more than a mathematical function, which receives input values and returns an output value based on the equation defined in said function.

A perceptron can also have defined input weights and a trend. The input weights can define if one input is more sensitive than another, and the trend, as its word indicates, is the value that the neuron tends to output. This will be further discussed later.

It should be noted how the error is key to learning since it is the error, and its measurement, which allows us to adjust weights and trends so that it decreases enough to consider an output as valid. If we didn't make mistakes we wouldn't learn.