Eigenvalues
2 long-form posts on Eigenvalues: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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The Price List, in JAX/Flax NNX
A runnable companion to the price-list post: kernel ridge in JAX, the representer solve (K + lambda I) alpha = y, the RKHS-norm bill alpha^T K alpha, the effective dimension d_eff = sum lambda_k/(lambda_k + lambda) from the Gram spectrum, and a generalization sweep that draws the U-curve. Every number and every figure is from one analytic solve, no gradient descent.
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Why Regularization Is a Price List
The representer theorem says the optimal weight is a sum over prototypes, but it does not explain why that sum generalizes. The answer is the RKHS norm: a price list that charges each prototype by its eigenvalue, and regularization is just tightening the budget. Four panels show the knob turning.