A Backpropagation learns by iteratively processing a set of training samples, comparing the network’s estimate for each sample with the actual known class label. For each training sample, weights are modified to minimize the mean squared error between the network’s prediction and the actual class. These changes are made in the “backward” direction, i.e., from the output layer, through each concealed layer down to the first hidden layer (hence the name backpropagation). Although it is not guaranteed, in general, the weights will finally converge, and the knowledge process stops.
