In this section, we will discuss what artificial neural networks are and their building blocks. We will learn how artificial neurons work and how they relate to their biological analogs. We will also discuss how to train neural networks with the backpropagation method, as well as how to deal with the overfitting problem.

A neural network is a sequence of neurons interconnected by synapses. The structure of the neural network came into the world of programming directly from biology. Thanks to this structure, the computer has the ability to analyze and even remember information. In other words, neural networks are based on the human brain, which contains millions of neurons that transmit information in the form of electrical impulses.

Artificial neural networks are inspired by biology because they are composed of elements with similar functionalities to those of biological neurons. These elements can be organized in a way that corresponds to the anatomy of the brain, and they demonstrate a large number of properties that are inherent in the brain. For example, they can learn from experience, generalize previous precedents to new cases, and identify significant features from input data that contain redundant information.

Now, let's understand the process of a single neuron.