What is the automatic differentiation mechanism in PyTorch?

The automatic differentiation mechanism of PyTorch refers to PyTorch’s ability to automatically calculate the gradient of tensors, i.e. the derivatives of tensors. This mechanism makes training deep learning models using PyTorch easier and more convenient because users do not need to manually write backpropagation algorithms to calculate gradients. PyTorch will automatically calculate gradients for users and update model parameters using gradients in the optimizer. This automatic differentiation mechanism is implemented through the autograd module in PyTorch, which records the operation history of tensors and automatically calculates gradients based on the chain rule. PyTorch’s automatic differentiation mechanism makes implementing deep learning models simpler and more efficient.

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