About This Book

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,
• and tiny ML systems become a way to make mathematical structure concrete.

This is not a finalized edition. Chapters, examples, terminology, diagrams, code, and references may evolve as the work continues.

Coauthors

Hamze Ghalebi

Hamze Ghalebi is a Paris-based AI architect, CTO, and software builder associated with Remo Lab. His work focuses on production GenAI, regulated AI systems, auditable AI products, Rust systems, and the transition from AI prototypes to reliable production architectures.

His background includes advanced study at Institut Polytechnique de Paris across statistics, optimization, machine learning, artificial intelligence, distributed systems, cloud computing, and data science.

Hamze brings the engineering and product perspective of the book: how to turn mathematical and machine-learning ideas into understandable, typed, maintainable systems. His current work is especially concerned with AI systems that can be evaluated, monitored, audited, and kept under human accountability in real operational environments.

In this book, his role is to connect tiny ML, Rust implementation, and production-minded software architecture — because apparently making category theory executable was not ambitious enough already.

Farzad Jafarranmani

Farzad Jafarranmani is a researcher and engineer in the Paris area, associated with Huawei and the Lagrange Mathematics and Computing Research Center. His work sits at the intersection of mathematics, computer science, logic, semantics, proof theory, and category theory.

He holds a PhD in Mathematics and Computer Science from Université Paris Cité, where his doctoral work focused on fixpoints of types in linear logic from a Curry–Howard–Lambek perspective. He also studied Mathematics and Computer Science at ENS Paris-Saclay, with work including induction in fibred multicategories and denotational semantics of linear logic with least and greatest fixpoints.

His previous research experience includes postdoctoral work at LIP6, Laboratoire d’Informatique de Sorbonne Université / CNRS, as well as a visiting research position at the University of Cambridge.

Farzad brings the mathematical and theoretical foundation of the book: category theory, denotational semantics, proof theory, type-theoretic structure, and the discipline required to keep abstractions precise instead of merely fashionable.

Public Feedback

Public feedback is welcome while the book is still growing.

Useful feedback includes unclear explanations, broken examples, missing references, awkward terminology, incorrect or overloaded mathematical language, Rust examples that could be clearer or more idiomatic, and places where the connection between Rust, machine learning, and category theory should be made more explicit.

This edition is intentionally public before it is final.

Use With Reference

Original material from this book may be quoted, reused, adapted, or taught from when the source is clearly referenced and both coauthors are credited.

Suggested reference format:

Ghalebi, H., & Jafarranmani, F.
Category Theory for Tiny ML in Rust.
Working Draft, Public Feedback Edition.

External works cited or referenced by this book remain under their own licenses, terms, and attribution requirements.