Backpropagation applies the chain rule layer by layer to propagate errors from outputs back to earlier layers. This makes it feasible to optimize many parameters with gradient descent, reviving interest in neural networks after earlier limitations were highlighted.

The technique laid groundwork for modern deep learning, where larger datasets, compute, and improved architectures extend the same core training principle.