Gradient descent is walking downhill on the loss surface, one step proportional to the slope. Knowing when to stop is the part that separates people who have trained models from people who have read about them: it is the validation curve, not the training curve, that tells you.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Gradient descent is walking downhill on the loss surface, one step proportional to the slope. Knowing when to stop is the part that separates people who have trained models from people who have read about them: it is the validation curve, not the training curve, that tells you.
The screen is two-part: a clean intuition for gradient descent (downhill on the loss surface, step size = learning rate, the gradient points uphill so you go opposite) AND a real stopping policy. Strong candidates stop on validation loss (early stopping with patience) rather than a fixed epoch count or training loss hitting zero, and mention plateaus, learning-rate schedules, and restoring the best checkpoint. The reserved follow-up is 'why not just train until training loss is near zero': because that is overfitting; training loss keeps falling while validation loss turns up, and the turn-up is the stop signal.
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