The document discusses backpropagation and how it is used to train multilayer perceptron neural networks. Backpropagation is a method used to calculate the gradient of the loss function with respect to the network parameters in order to update the weights in the direction of steepest descent during training. It works by propagating errors backwards from the final layer through the network to earlier layers to compute sensitivity values. The weights are then updated using these sensitivities and the gradient of the loss function to minimize error. An example 1-2-1 network is also described to illustrate forward propagation, backpropagation of errors to compute sensitivities, and weight updates.