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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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Recent Posts
- Why Your Classifier Trains So Slowly at First (and MSE Might Be Why)
- How a Neural Network Actually Learns: From a Single Neuron to Backpropagation
- Why Cross-Entropy Beats MSE for Classification: The Gradient Math, Proven (Ep:03.06)
- Mini-Batch Gradient Descent Explained: Why Batching Is Just Matrix Multiplication (Ep:03.05)
- Backpropagation From Scratch: Deriving the Four Equations, Verified Against PyTorch (Ep:03.04)
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