Explain how backpropagation works in training a neural network. What is the role of the chain rule, and how are gradients used to update weights?

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Backpropagation, short for backward propagation of errors, is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight in the network. These gradients indicate how to adjust weights to reduce prediction errors.