Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material.

Learn how to implement backpropagation using automatic differentiation from the ground up in Python—no libraries, just pure ...

Build your own backpropagation algorithm from scratch using Python — perfect for hands-on learners!

However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information—and therefore storage—of the partial derivatives of the weight values ...

Hinton's motivation for the algorithm is to address some of the shortcomings of standard backpropagation training which requires full knowledge of the computation in the forward pass to compute ...