It applies the chain rule from the output layer backward through the computation graph. The resulting gradients are passed to an optimizer, which decides how to update the parameters.
Backpropagation efficiently computes how a neural network's loss depends on each trainable parameter.
It applies the chain rule from the output layer backward through the computation graph. The resulting gradients are passed to an optimizer, which decides how to update the parameters.