Backpropagation

The algorithm used to train neural networks by calculating how much each parameter contributed to an error, then adjusting weights accordingly. It works by propagating the error signal backwards through the network layer by layer using the chain rule of calculus, and is the engine behind nearly all modern deep learning.
See also: gradient descent, activation function, loss function.