Backpropagation is a supervised learning algorithm used in multi-layer artificial neural networks that computes the gradient of a loss function with respect to each weight by applying the chain rule of calculus recursively from the output layer back through hidden layers. The core mechanism relies on calculating local gradients at individual nodes and propagating error signals backward, enabling efficient optimization via Stochastic Gradient Descent (SGD) or its variants. This method forms the foundational training paradigm within the subfield of deep learning, distinct from unsupervised contrastive methods or direct feed-forward inference without weight updates.
Backpropagation is a differentiable computational mechanism for calculating gradients in multi-layer neural networks by recursively applying the chain rule to decompose global cost sensitivities into…