automatic differentiation in deep learning implementing automatic differentiation let' now consider how automatic differentiation is implemented within pytorch the preceding example was very simplethings would be really complicated as we explore the approach on paper for large functions ( deep learning functionsin most common networksthe number of parameters that would be involved is very highmaking manually programming the computation of gradients herculean task pytorch provides the autograd packagewhich essentially simplifies the entire process for us recall the loss backward(function that we leveraged in for the toy neural network the network computes all the necessary gradients for the loss with respect to the weights let' explore this further what is autogradthe autograd package within pytorch provides automatic differentiation for all operations on tensors it performs the necessary computations within backpropagation for our neural network when the backward(function is calledthe module computes all the backpropagation gradients automatically we can also access individual gradients through variable' grad attribute the autograd module provides ready to use tools (functions/classesfor implementing automatic differentiation of arbitrary scalar valued functions to enable gradients to be computed for variablewe need only to set the value as true for the keyword requires_grad let' replicate the same example we used to manually implement automatic differentiation but using pytorch (listing -