Debugging Neural Networks
Your network compiled. It ran. It produced a number. The number is wrong and nothing crashed. Welcome to the hardest kind of debugging -- the kind where there is no error message. Diagnose common neural network failures (NaN loss, flat loss curve, overfitting, oscillation) using systematic debugging strategies. Apply the "overfit one batch" technique to verify that your model architecture and training loop are correct. Inspect gradient magnitudes, activation distributions, and weight norms to identify vanishing/exploding gradient problems. Build a debugging checklist that covers data pipeline, model architecture, loss function, optimizer, and learning rate issues. Traditional software crashes when it is broken. A null pointer throws an exception. A type mismatch fails at compile time. An off-by-one error produces a clearly wrong output. Neural networks do not give you that luxury. A broken neural network runs to completion, prints a loss value, and outputs predictions. The loss might decrease. The predictions might look plausible. But the model is silently wrong -- learning shortcuts, memorizing noise, or converging to a useless local minimum. Google researchers estimated that 60-70% of ML debugging time is spent on "silent" bugs that produce no errors but degrade model quality. The difference between a working model and a broken one is often a single misplaced line: a missing zerograd(), a transposed dimension, a learning rate off…
Debugging Neural Networks: Your network compiled. It ran. It produced a number. The number is wrong and nothing crashed. Welcome to the hardest kind of…
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