Deep Learning with Keras

A practical overview of backpropagation

Multilayer perceptrons learn from training data through a process called backpropagation. The process can be described as a way of progressively correcting mistakes as soon as they are detected. Let's see how this works.

Remember that each neural network layer has an associated set of weights that determines the output values for a given set of inputs. In addition to that, remember that a neural network can have multiple hidden layers.

In the beginning, all the weights have some random assignment. Then the net is activated for each input in the training set: values are propagated forward from the input stage through the hidden stages to the output stage where a prediction is made (note that we have ...