Common PyTorch training mistakes cause silent model degradation

Developers frequently make subtle implementation errors in PyTorch training loops — such as forgetting .zero_grad(), not toggling train/eval mode, or applying softmax before CrossEntropyLoss — that silently degrade model quality without raising errors. These mistakes are hard to detect and can waste significant compute time before being caught.

Sources

• PYTORCH COMMON MISTAKES - How To Save Time 🕒

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