Building ML framework with Rust and Category Theory

Category Theory for Tiny ML in Rust A practical bridge between compositional mathematics, Rust types, and tiny machine-learning systems Working Draft · Public Feedback Edition Coauthored by

Hamze Ghalebi

Farzad Jafarranmani GitHub repository

Category Theory for Tiny ML in Rust is a working draft that develops a small, explicit machine-learning system through the lens of category theory and Rust.

The book is designed for readers who want to understand machine learning not only as numerical computation, but as a structured pipeline of objects, transformations, composition, and constraints.

Rather than treating category theory as decorative abstraction, this book uses it as an engineering tool:

domain objects become Rust types,

morphisms become typed transformations,

composition becomes executable program structure,

training becomes repeated transformation of model state,

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