Backpropagation
Backpropagation is the fundamental algorithm used for efficiently training Artificial Neural Networks (ANNs). Essentially, it is a method for calculating the network's prediction error relative to the correct target values and using that error to adjust the network's weights and biases. The process involves two phases: first, the prediction is sent forward (forward pass), and then the error is propagated backward (backward pass) through the network. This backward propagation allows the algorithm to determine how much each neuron and weight contributed to the final error, enabling gradual improvement.
