Quiz · 8 questions
🧮 Autograd — Automatic Differentiation
The thing that makes neural networks trainable. Dynamic graphs, backward passes, and the gotchas you'll hit.
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Quiz
01Why is reverse-mode AD the right choice for training neural networks?
02Why does every training loop have
optimizer.zero_grad()?03Which is the modern, recommended context manager for pure inference?
04After backward(), why is
b.grad None for an intermediate tensor b?05What is the modern API for mixed-precision training?
06When using bfloat16 autocast, do you typically need a GradScaler?
Hint
What problem does GradScaler solve, and does bf16 have it?
07Where in the training loop should gradient clipping go?
08What does
torch.func.vmap let you do most cleanly?Comments 0
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