Backpropagation
Backpropagation is a supervised learning algorithm used for training neural networks. It is widely considered a crucial part of the neural network's training process.
How It Works
Backpropagation involves two major steps: forward pass and backward pass. During the forward pass, the input data is passed through the network to get an output. In the backward pass, the output error is propagated back through the network to update the weights.
Mathematical Formulation
The algorithm calculates the gradient of the loss function with respect to each weight by the chain rule, iteratively minimizing the error.