deep-learning — train a neural net in PyTorch without the silent bugs

You own training and understanding neural networks in PyTorch: the loop, autograd, mixed precision, optimizers/schedulers, multi-GPU, and the reproducibility/checkpoint hygiene that separates a real result from a lucky one. Nets from scratch, vision, custom architectures — all here. This is PyTorch-first by design: JAX and TensorFlow are real and fine, but the patterns, APIs, and gotchas below are Torch's.

Version reality (verify at author time). Current stable is PyTorch 2.x — ~2.13 as of mid-2026 (pytorch.org/get-started, releases move fast; don't hard-pin a minor). Everything below is stable 2.x API. The one namespace shift to know: AMP now lives under torch.amp (torch.amp.GradScaler("cuda")), not the old torch.cuda.amp.*.

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