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Debugging training curves

Reading **train vs validation loss/accuracy curves** to diagnose underfit, overfit, instability, and data bugs.

What it is

Reading train vs validation loss/accuracy curves to diagnose underfit, overfit, instability, and data bugs.

Why it matters

Curves are the X-ray of training. Most “mystery failures” show up here first.

How it works (plain)

Both high → underfit/capacity/LR/data. Train ↓ val ↑ → overfit. Wild spikes → LR/bugs/bad batches. Train not moving → dead net/wrong loss/labels.

Everyday example

A fitness tracker: if resting heart rate trends contradict workouts, investigate sensors before diets.

Try it

Sketch four curve patterns and label the first fix you’d try for each.

Myths

⚠️ Myth: Smooth curves mean a correct pipeline.
✓ Reality: Smooth wrong labels still look smooth.

Sources