PyTorch Demystified, Why Did I Switch
summary
PyTorch claims practical wins from a Dynamic Computation Graph (autograd/Variable, .grad access, .cuda and NumPy bridges) and demonstrates them with an actual stack‑based sentence classifier that uses a reduction network and a transition list over 500k sentences to build varying graphs on the fly. Benchmarks are anecdotal but no slowdown appears and a face‑recognition system migrates to PyTorch and runs in production. Code is available under MIT.
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chapters (10)
- 0:00Introduction
- 1:00Computational graphs
- 4:15Static vs dynamic graphs
- 7:27Getting started with PyTorch
- 10:05Understanding autograd
- 14:55Why switch to PyTorch
- 17:32Power of dynamic graphs
- 23:12Implementation and debugging
- 27:16Bonus points and conclusion
- 30:29Q&A session
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