ML Math: Zero to Hero

Interactive mathematics for machine learning — from scalars to diffusion models. Explained from first principles, built for engineers who want to understand the math, not just use it.

Part I

Foundations

Scalars, Vectors, Matrices, Functions & Graphing

Part II

Calculus Engine

Derivatives, Chain Rule, Gradient Descent

Part III

Probability & Loss

Probability Theory, Loss Functions

Part IV

Learning Systems

Neural Networks, PCA, Multivariable Calculus, Optimization

Part V

Statistical Inference

Bayes' Theorem, Maximum Likelihood, Information Theory

Part VI

Modern Architectures

Attention Mechanism, Convolutions, VAEs, Diffusion, RL

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Chapters

Parts

∞

Interactive demos