Why back propagation goes backward
Backpropagation, the algorithm used to train deep neural networks, computes gradients by moving from the output layer toward the input layer. The method applies the chain rule of calculus to propagate error signals backward, updating each weight based on its contribution to the final loss. This reverse traversal enables efficient learning across many layers, forming the core of modern AI training.