What mathematical rule is the fundamental foundation of the backpropagation algorithm used to train neural networks?
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Chain rule
Backpropagation uses the chain rule of calculus to compute the gradient of a loss function with respect to the weights of the network. This allows the error to be propagated backward from the output layer to the input layer efficiently.
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- The backpropagation algorithm relies on which calculus rule to calculate gradients in a neural network?