Tutorial Notes: Scaling Laws, data requirements, and the math behind model improvement
PTenigma · x · 2026-08-23
The author released new tutorial notes on AI Scaling Laws to explain how models improve with more data.
Key Topics:
- Determining data requirements for training classifiers (e.g., brain disease diagnosis via MRI).
- Performing power calculations for AI models.
- Analyzing confidence limits on accuracy when training on N samples.
- Mathematical principles from spectral theory (Wigner's "spectral horizon") and Malliavin calculus, modeling model improvement as a stochastic process similar to the stock market.
These notes will serve as the basis for an upcoming video lecture series.
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