Let's start by defining Artificial Neural Networks (ANN) with a number of logical steps, rather than a classic monolithic sentence using obscure jargon with an even more obscure meaning.

First of all, an ANN is a statistical model. What is a statistical model? A statistical model is a pair of elements, namely the space S (a set of observations) and the probability P, where P is a distribution that approximates S (in other words, a function that would generate a set of observations that is very similar to S).

I like to think of P in two ways: as a simplification of a complex scenario, and as the function that generated S in the first place, or at the very least a set of observations very similar to S.

So ANNs are models that take a complex reality, simplify it, and deduce a function to (approximately) represent statistical observations one would expect from that reality, in mathematical form.

The next step in our journey towards comprehending ANNs is to understand...