The Chain Rule of Calculus – Even More Functions
The chain rule is an important derivative rule that allows us to work with composite functions. It is essential in understanding the workings of the backpropagation algorithm, which applies the chain rule extensively in order to calculate the error gradient of the loss function with respect to each weight of a neural network. We will be building on our earlier introduction to the chain rule, by tackling more challenging functions.
In this tutorial, you will discover how to apply the chain rule of calculus to challenging functions.
After completing this tutorial, you will know:
- The process of applying the chain rule to univariate functions can be extended to multivariate ones.
- The application of