September 2019
Intermediate to advanced
416 pages
13h 49m
English
In this appendix, we’ll introduce the distinguishing elements of PyTorch, including contrasting it with its primary competition—TensorFlow.
In Chapter 14, we introduced PyTorch at a high level. In this section, we continue by examining the library’s core attributes.
PyTorch operates using what’s called an autograd system, which relies on the principle of reverse-mode automatic differentiation. As detailed in Chapter 7, the end product of forward propagating through a deep neural network is the result of a series of functions chained together. Reverse-mode automatic differentiation applies the chain rule to differentiate the inputs with respect to the cost at the end, working backwards (introduced ...