by propagating derivatives backward, one layer at a time, from the output layer to the input layer, thereby avoiding redundant chain-rule calculations.

Strictly speaking, the term backpropagation refers only to an algorithm for efficiently computing the gradient, not how the gradient is used, but the term is often used loosely to refer to the entire learning algorithm. This includes changing model parameters in the negative direction of the gradient, such as by stochastic gradient descent,