The Chain Rule of Calculus for Univariate and Multivariate Functions

The chain rule allows us to find the derivative of composite functions. It is computed extensively by the backpropagation algorithm, in order to train feedforward neural networks. By applying the chain rule in an efficient manner while following a specific order of operations, the backpropagation algorithm calculates the error gradient of the loss function with respect to each weight of the network. In this tutorial, you will discover the chain rule of calculus for univariate and multivariate functions. The Chain Rule of Calculus for Univariate and Multivariate Functions Photo by Pascal Debrunner, some rights reserved.