What is backpropagation, and how does PyBrain implement it?
Backpropagation is a supervised learning algorithm used for training artificial neural networks, especially feedforward neural networks.
It consists of two main phases: forward pass and backward pass.
During the forward pass, the input data is propagated through the network, layer by layer, to compute the output. Then, during the backward pass, the error between the predicted output and the true target output is calculated, and this error is used to update the network's weights in the direction that minimizes the error.
In PyBrain, backpropagation is implemented in the BackpropTrainer class, which is part of the pybrain.supervised.trainers module. This class provides a simple and efficient implementation of the backpropagation algorithm for training feedforward neural networks.
