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Backpropagation is a core algorithm used to train artificial neural networks. It works by propagating the error from the output layer back through the network to adjust the weights of the connections. This is done using gradient descent, where the network calculates the gradient of the loss function with respect to each weight and updates them to reduce prediction error. Backpropagation enables deep learning models to learn complex patterns by refining internal parameters through multiple training iterations.
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