How does the Pytorch implementation of backpropagation compare to the backpropagation algorithm described in the deep learning book?
It section 6.5.6 of the deep learning book 11 a back-propagation algorithm is described.
However I would think that Pytorch implements a different algorithm. Because in Pytorch a tensor does not have access to its consumers, is that correct?
If it is different, then how does it compare?
Kind regards,
Jens