Chain Rule

The chain rule makes it possible to compute derivatives in layered functional structures, which is essential in deep learning. The effect of each layer is combined with the derivative of the next one so that the full learning signal can be propagated backward. Backpropagation is theoretically built on this principle. Understanding the chain rule is therefore fundamental to understanding why and how deep networks can learn.