A neural network is a function that learns patterns from examples. It’s made of “neurons” organized in layers—input takes data in, hidden layers process it, and output gives you the answer. Each connection between neurons has a “weight” that determines how strongly one neuron influences the next.
At first, the network makes wild guesses. But here’s the magic: it compares its guess to the right answer, calculates how wrong it was, and tweaks those weights slightly to be less wrong next time. Repeat this billions of times with millions of examples, and the weights slowly converge to capture real patterns—like “this picture has a cat” or “this word should come next.”
It’s a bit like learning to catch a ball. You don’t calculate physics—you just try, miss, adjust your hands, try again, and eventually your brain learns the right feel. The network doesn’t “understand” anything. It just finds weights that work.