Recall first diagnostic moves for common neural-network training failures.
Loss became NaN after a few hundred steps. A teammate wants to rewrite the model. First line Check input values, loss inputs, learning rate, and gradient norms before changing architecture. NaN is often a numerical or update-size issue, not immediate proof the architecture is wrong. It tests the signal path and update stability first. Slow learning: learning rate or capacity? First fork If a tiny clean batch cannot learn, debug optimization or data before capacity. Training accuracy high, validation weak. Name the treatment family. Generalization response Use early stopping, regularization, augmentation, simpler capacity, or better data. Do not celebrate the…
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