Backpropagation
The algorithm that trains neural networks by propagating error gradients.
Backpropagation computes how much each weight in a network contributed to its error, by applying the chain rule of calculus backward from the output. These gradients are then used to update the weights. Popularized in 1986, it is the workhorse that makes training deep networks practical.