How Does a Neural Network Actually Learn?

A neural network learns by trying many different answers until it finds one that works, like a kid guessing a game's rule and getting better each time they play.

Imagine you're playing a game where you have to guess what color a fruit is based on how big or small it looks. At first, you might say "big means red", but then you see a big green apple. That guess was wrong! So you try again: maybe "small means yellow," but then you see a tiny red cherry. Hmm… you keep trying different guesses until you get better at knowing which color matches which size.

In the same way, a neural network tries many different answers, like a kid testing out rules, and gets better each time it makes a mistake. It uses numbers to represent its guesses, and every time it's wrong, it adjusts those numbers just a little bit, like tweaking your guess when you see a new fruit.

Think of the network as a group of smart kids working together, each one has their own favorite way to guess. They all try different answers at once, and then they compare notes. If someone guesses wrong, they change their answer just a little bit, like saying "maybe I should think about size more next time." This process happens again and again until everyone agrees on the best answer, and that's when the network learns!