Web Reference: Backpropagation efficiently computes the gradient of the loss with respect to the network weights for a single input–output example. It does this by propagating derivatives backward, one layer at a time, from the output layer to the input layer, thereby avoiding redundant chain-rule calculations. 3 days ago · Backpropagation is an algorithm that trains neural networks by reducing prediction error. It works by propagating errors backward, computing gradients using the chain rule, and updating weights and biases to improve performance. This is the whole trick of backpropagation: rather than computing each layer’s gradients independently, observe that they share many of the same terms, so we might as well calculate each shared term once and reuse them. This strategy, in general, is called dynamic programming.
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