Backpropagation, short for "backward propagation of errors," is a fundamental algorithm used in training neural networks, particularly in the context of supervised learning. It plays a crucial role in the learning process by optimizing the weights of the network to minimize the difference between actual and predicted outputs. Here's a breakdown of its key aspects:
This process is repeated iteratively over many epochs (complete passes through the training dataset), gradually improving the model's performance. Backpropagation is an essential part of most modern neural network training techniques and is the backbone of the remarkable capabilities seen in deep learning models.
Webdesk AI Glossary : Backpropagation