Backpropagation

Backpropagation is the cornerstone algorithm for training the vast majority of Artificial Neural Networks (ANNs), especially deep learning models. It is a supervised learning algorithm that efficiently calculates the gradient of the loss function with respect to the weights of the network, which is then used to adjust those weights to minimize the error. The process involves two main phases: a forward pass where input data is propagated through the network to produce an output and calculate the overall error, and a backward pass where this error signal is propagated backward through the network.