Chain Rule

In the context of machine learning, the chain rule is a method used to calculate the derivative of a composite function. It is used extensively in backpropagation, which is a key algorithm for training neural networks. Backpropagation involves calculating the gradient of the loss function with respect to the weights of the neural network. The chain rule allows us to calculate this gradient efficiently by breaking down the calculation into smaller, simpler steps. Specifically, the chain rule states that the derivative of a composite function is equal to the product of the derivatives of its individual functions. This allows us to calculate the gradient of the loss function with respect to each weight in the neural network by working backwards through the network, calculating the derivative of each layer with respect to the previous layer.
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