Phase 4 · Deep learning · Training
Debugging training
Read loss curves and fix what's wrong.
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Why it matters
Neural-network bugs rarely crash. The code runs, the loss prints, and the model is quietly wrong: a missing line, a shuffled label, a learning rate off by 100×. Experienced practitioners aren't the ones who avoid bugs; they're the ones with a routine for finding them. This lesson is that routine, and it applies just as much when a fine-tuning job or an eval "doesn't work".
What you'll be able to do
- Recognize the loss-curve signatures of common training bugs
- Run the standard sanity checks: initial loss, overfitting one batch, a baseline
- Narrow a problem down systematically instead of changing many things at once
- Diagnose a failing run from its numbers
Before this lesson
Next in this phase: Residual connections