Gradients support in PyTorch

In this article by Maxim Lapan, the author of Deep Reinforcement Learning Hands-On,we are going to discuss about gradients in PyTorch. Gradients support in tensors is one of the major changes in PyTorch 0.4.0. In previous versions, graph tracking and gradients accumulation were done in a separate, very thin class Variable, which worked as a wrapper around the tensor and automatically performed saving of the history of computations in order to be able to backpropagate. Now gradients are a built-in tensor property, which makes the API much cleaner. Gradient was originally implemented in the Caffe toolkit and then became the de-facto standard in DL libraries.