Verified65% confidenceFactTime unknown
When loss.backward() is called, PyTorch traverses the computation graph in reverse, using the chain rule to automatically calculate the gradient of each parameter.
3
Sources
65%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
Efficient PyTorch Learning: A Source Code-Driven Methodology
bilibili论文不秃头指南
Related Entities
Related Claims
UnverifiedAutomatic differentiation dynamically constructs a directed acyclic graph (computation graph) by recording each operation and its inputs during every forward computation, and during backpropagation it traverses the graph's reverse edges, sequentially invoking each operation's local gradient function to accumulate gradients via the chain rule76% similarUnverifiedPyTorch and TensorFlow fully automate gradient computation through autograd.68% similarVerifiedPyTorch uses a dynamic computation graph (Define-by-Run), tracking tensor operations via a directed acyclic graph (DAG) to enable automatic differentiation.64% similar
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