Backpropagation is the algorithm that trains neural networks by computing how much each weight contributed to the error and adjusting it accordingly, layer by layer from output back to input. It is the reason deep learning works at all: without it, networks could not learn from their mistakes. Imagine a student handing in an exam, receiving corrections, and tracing each wrong answer back to the exact concept they misunderstood, then adjusting their understanding.