DIY PyTorch - How to Learn Deeply
summary
Explains how PyTorch’s implementation reduces deep learning to function composition and operator blocks, exposing a forward trace and an adjoint computational graph that runs backward to collect gradients, and claims this makes training feasible from thousands to billions of parameters; notes PyTorch is ~3 million lines and uses tensors on GPU for performance; builds a schematic that overlays forward/backward passes, shows space-saving optimizations, and highlights reverse-mode automatic differentiation as the key.
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chapters (7)
- 0:00PyTorch introduction
- 2:10Functions and compositions
- 6:02Function compositions in deep learning
- 9:35Reverse mode autodiff
- 13:52Adjoint computational graphs
- 15:18Q&A
- 17:44Tensors and GPU architecture
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