Backpropagation Algorithm

Backpropagation is a method used in training neural networks to improve their accuracy. It involves measuring how much the network's output differs from the desired result (error) and then adjusting the connections (weights) between neurons to reduce this error. The process works backward from the output layer to earlier layers, updating weights based on the error’s influence. This iterative process continues until the network's predictions become more accurate. Essentially, backpropagation helps the network learn by fine-tuning its internal parameters to better recognize patterns and make correct predictions.