automatic differentiation in deep learning while exploring stochastic gradient descent in we treated the computation of gradients of the loss function xl(xas black box in this we open the black box and cover the theory and practice of automatic differentiationas well as explore pytorch' autograd module that implements the same automatic differentiation is mature method that allows for the effortless and efficient computation of gradients of arbitrarily complicated loss functions this is critical when it comes to minimizing loss functions of interestat the heart of building any deep learning model lies an optimization problem that is invariably solved using stochastic gradient descentwhichin turnrequires one to compute gradients automatic differentiation is distinct from both numerical and symbolic differentiation we start by covering enough about both of these so that distinction becomes clear for the purposes of illustrationassume that our function of interest is and we intend to find the derivative of fdenoted by '( (cnikhil ketkarjojo moolayil ketkar and moolayildeep learning with python