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Why Your Classifier Trains So Slowly at First (and MSE Might Be Why)
A classification model that’s confidently wrong early in training can get an almost nonexistent correction signal if you’re using the wrong loss function. Here’s the exact mechanism, measured, and why…
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Why Cross-Entropy Beats MSE for Classification: The Gradient Math, Proven (Ep:03.06)
A confidently wrong sigmoid+MSE network gets a weaker correction signal exactly when it needs a stronger one. Module 3 of From Zero to Agents derives why softmax+cross-entropy avoids this pathology,…
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Softmax and Cross-Entropy Explained: The Loss Function Behind Every LLM (Ep:02.04)
Softmax turns raw scores into a probability distribution; cross-entropy measures how wrong that distribution is. Module 2 of From Zero to Agents derives both from probability theory, proves the famous…
