Backpropagation Learning Path

Definition and purpose of backpropagation

Backpropagation is a gradient-computation method used to train neural networks. Its purpose is to efficiently calculate how the loss (error) changes with respect to each network weight for a given input–output example. It does this by applying the chain rule in an efficient “reverse” way: derivatives are propagated backward from the output layer to the input layer, avoiding redundant calculations. In practice, backpropagation is often used together with an optimization step such as gradient descent. After computing the gradient of the loss with respect to the weights, the learning algorithm updates the parameters in the direction that reduces the loss (e.g., moving weights in the negative gradient direction). Although “backpropagation” strictly refers to the gradient-calculation algorithm, the term is commonly used loosely to include the full training procedure (gradient computation plus parameter updates).

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Gradient computation via chain rule

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