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 ...