Why Your First PyTorch Model Probably Won't Train
Your loss curve flatlines at 0.693. The gradients are all NaN after epoch 2. Or worse — training completes without errors, but your model predicts the same class for every input.
I've seen these failure modes dozens of times, and they all trace back to the same handful of setup mistakes. PyTorch gives you enough rope to hang yourself: it won't stop you from initializing weights incorrectly, forgetting to zero gradients, or using the wrong loss function for your task. The training loop runs, the progress bar fills up, and you only realize something's wrong when you check the outputs.
This isn't a gentle introduction to neural networks. It's a focused look at the five PyTorch-specific mistakes that silently break training, why they happen, and how to fix them before you waste GPU hours. I'm assuming you know what backpropagation is and have written at least one forward() method. If you're still Googling "what is a tensor," start with the official PyTorch tutorials and come back.
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