called back-propagation—for training these networks. As its name implies, back-propagation is a way to take an error observed at the output units (for example, a high confidence for the wrong digit in the example of figure 4) and to “propagate” the blame for that error backward (in figure 4, this would be from right to left) so as to assign proper blame to each of the weights in the network. This allows back-propagation to determine how much to change each weight in order to reduce the error.
