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πŸ”·Allβˆ‘Mathβš™οΈAlgoπŸ—‚οΈDSπŸ“šTheory

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#normal equations
πŸ“šTheoryIntermediate

Scaling Laws

Scaling laws say that model loss typically follows a power law that improves predictably as you increase parameters, data, or compute.

#scaling laws#power law#chinchilla scaling+12
πŸ“šTheoryIntermediate

Bias-Variance Tradeoff

The bias–variance tradeoff explains how prediction error splits into bias squared, variance, and irreducible noise for squared loss.

#bias variance tradeoff#mse decomposition#polynomial regression+12