automatic differentiation in deep learning output[default value for requires_grad for xfalse updated value for requires_grad for xtrue final value for function tensor([- ]grad_ fn=gradient value for xtensor([ ]gradient value for ytensor([ ]gradient value for ztensor([- ]the gradient values here match exactly with what we computed manually earlier in the preceding examplewe first created tensor and then assigned the keyword for requires_grad as true we can also combine this along with our definition torch autograd variable(torch tensor([ ]),requires_ grad=truewhile we define network in pytorcha lot of these details are taken care of when we define network layerwith nn linear( (refer to the example)pytorch creates the weight and bias tensor with the necessary values (setting requires_grad as truethe input tensors did not need the gradientshencewe never set them in our example and used the default ( falses ummary this covered the basics of automatic differentiation backpropagation is special case of automatic differentiation used in training deep neural networks in modern deep learning literatureautomatic differentiation is analogous to backpropagationas it more