⚙️ AI Hardware

Cross-Entropy Loss: Derived from Probability, Not Pulled from Thin Air—And Why It Still Fails You

95% of top Kaggle classifiers run on cross-entropy loss. But do their creators know it's just maximum likelihood dressed in optimizer clothes? Let's tear it apart.

Aisha Patel 📅 Mar 22, 2026 ⏱️ 3 min read 👁️ 6 views
Visualization of probability distributions leading to machine learning loss functions like cross-entropy

⚡ Key Takeaways

  • Cross-entropy derives directly from categorical likelihood—no magic, just math.
  • Poisson loss fits counts perfectly, crushing Gaussian on skewed data.
  • Blindly using defaults ignores assumptions; test or fail in production.

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Aisha Patel
Written by

Aisha Patel

Former ML engineer turned writer. Covers computer vision and robotics with a practitioner perspective.

#MLE #cross-entropy-loss #neural-network-training #poisson-loss

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Originally reported by Towards AI

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