Backpropagation is the method neural networks use to learn: after the model makes a prediction, it measures how wrong it was (the loss) and then efficiently computes how much each weight contributed to that error using the chain rule from calculus. Those “error signals” are propagated backward through the layers to produce gradients, and an optimizer (like gradient descent) uses those gradients to nudge the weights so the loss decreases on the next attempt.