Why Adam’s Default Settings Can Tank Your Models—and How to Fix It
The piece uncovers three hidden failure modes of the Adam optimizer—learning‑rate decay, bias‑correction glitches, and a mis‑tuned epsilon—that can derail training. It backs the claims with concrete experiments and then hands you exact hyperparameter tweaks to keep Adam reliable, saving hours of wasted runs.