Prerequisite chain
Prerequisites for Lazy vs Feature Learning
Topics you need before working through Lazy vs Feature Learning. Direct prerequisites are listed first; transitive prerequisites (the chain reachable through them) follow.
Direct prerequisites (2)
- Neural Tangent Kernel: Lazy Training, Kernel Equivalence, μP, and the Limits of Widthlayer 4, tier 1
- Mean Field Theorylayer 4, tier 2
Reachable through the chain (30)
These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.
- Kernels and Reproducing Kernel Hilbert Spaceslayer 3, tier 2
- Convex Optimization Basicslayer 1, tier 1
- Differentiation in Rnlayer 0A, tier 1
- Sets, Functions, and Relationslayer 0A, tier 1
- Basic Logic and Proof Techniqueslayer 0A, tier 2
- Vectors, Matrices, and Linear Mapslayer 0A, tier 1
- Continuity in Rⁿlayer 0A, tier 1
- Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
- Matrix Operations and Propertieslayer 0A, tier 1
- Rademacher Complexitylayer 3, tier 1
- Empirical Risk Minimizationlayer 2, tier 1
- Concentration Inequalitieslayer 1, tier 1
- Common Probability Distributionslayer 0A, tier 1
- Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
- Random Variableslayer 0A, tier 1
- Kolmogorov Probability Axiomslayer 0A, tier 1
- VC Dimensionlayer 2, tier 1
- Ridge Regressionlayer 1, tier 1
- Linear Regressionlayer 1, tier 1
- Maximum Likelihood Estimation: Theory, Information Identity, and Asymptotic Efficiencylayer 0B, tier 1
- Central Limit Theoremlayer 0B, tier 1
- Law of Large Numberslayer 0B, tier 1
- KL Divergencelayer 1, tier 1
- Information Theory Foundationslayer 0B, tier 2
- Shrinkage Estimation and the James-Stein Estimator: Inadmissibility, SURE, and Brown's Characterizationlayer 0B, tier 1
- Cramér-Rao Bound: Information Inequality, Achievability, and Sharper Variantslayer 0B, tier 1
- Fisher Information: Curvature, KL Geometry, and the Natural Gradientlayer 0B, tier 1
- Minimax Lower Bounds: Le Cam, Fano, Assouad, and the Reduction to Testinglayer 3, tier 1
- Implicit Bias and Modern Generalizationlayer 4, tier 1
- Gradient Descent Variantslayer 1, tier 1