**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:

**Forward Pass:**Initially, inputs are passed through the neural network layer by layer (forward pass) to produce an output.**Error Calculation:**The output is then compared to the expected result, and the difference (error) is calculated, usually using a loss function.**Backward Pass:**In the backpropagation phase, this error is propagated back through the network in the opposite direction. This process involves calculating the gradient of the error with respect to each weight in the network, using techniques from calculus (chain rule).**Weights Update:**The calculated gradients are used to update the weights of the network, with the aim of reducing the error. The size of the update is governed by a parameter known as the learning rate.

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