How Neural Networks Learn: The Magic of Backpropagation

Descrizione dell'autore

At first, a neural network starts with random, messy guesses—it has no idea what a bird or a cat is. But through a powerful process called backpropagation, it learns. When the network makes a wrong prediction (say, it thinks a bird is a cat), it uses calculus to trace back through its connections and ask: "How should I adjust these weights to be more right next time?" That’s the core of deep learning: making small corrections again and again until the network gets it right. It’s not magic—it’s math.