Backpropagation: A Powerful Algorithm for Training Neural Networks

What is Backpropagation?

Backpropagation is an iterative training algorithm used to train artificial neural networks, specifically in feed-forward neural networks. Its purpose is to minimize the error between the network's predicted output and the desired output.

How Does Backpropagation Work?

During the forward pass, the input is fed through the network to generate an output. The difference between this output and the desired output is the error. In the backward pass, the error is propagated backward through the network, calculating the gradients of the cost function with respect to each weight and bias. These gradients determine the direction and magnitude of adjustments required to minimize the error.