This document discusses backpropagation, an algorithm used to train feedforward neural networks. It begins by explaining gradient descent and how it is used to minimize error in the network by adjusting weights. It then describes how backpropagation specifically works to calculate the gradient of the error with respect to the weights in each layer by propagating error backwards from the output layer through the hidden layers. The general backpropagation rule is provided to update weights based on this error gradient calculation.