How does the backpropagation algorithm work in training a multi-layer neural network for deep learning?

Backpropagation is a supervised learning algorithm used for training multi-layer neural networks. It involves a forward pass where inputs are propagated through the network to generate an output, and a backward pass where the error is propagated back through the network to update the weights. The algorithm uses gradient descent to minimize the cost function by adjusting the weights in the direction that reduces the error.

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