Chapter 3: Automatic Differentiation with Autograd in PyTorch
Abstract : PyTorch's autograd package provides automatic differentiation for all operations on Tensors, forming the backbone of neural network training in PyTorch. It operates as a define-by-run framework, meaning the backpropagation process is dynamically defined by the execution of your code. Here's how automatic differentiation with autograd works in PyTorch: Tensors with requires_grad=True : To enable autograd to track operations and compute gradients for a specific tensor, you must set its requires_grad attribute to True . This signals to PyTorch that this tensor is part of a computation for which gradients need to be calculated. Python import torch x = torch.tensor( 2.0 , requires_grad= True ) y = torch.tensor( 3.0 , requires_grad= True ) Building the Computation Graph : As operations are performed on tensors with requires_grad=True , autograd impl...