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.
The current public edition is still worth publishing: readers can use the existing chapters, run the Rust examples, and send evidence-shaped feedback while later completion passes continue.
The public source repository is available at github.com/hghalebi/category_theory_transformer_rs.
Public Workshop
The first public workshop for this book and Rust lab is hosted through AI Reading Club. It introduces the tiny ML pipeline as typed Rust structure and uses the working draft as the shared study material.
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.
Feedback is easiest to act on when it is opened in the GitHub repository with a specific chapter, command, or source file.
Use the public review path if you want a short route for reviewing the book. If you want the shareable public reviewer call, use Reviewers Needed. If several readers are reviewing together, use the public review sprint to split reports across Rust, ML, category-theory, educator, and beginner perspectives. Use the chapter clarity form when you can name the first unclear sentence, output line, table row, code block, or exercise.
If you are reading the online book and cannot clone the repository right now,
you can still review one public page. Read Welcome, Course Map, Domain Objects,
or Morphism and Composition, then report the first visible sentence, heading,
diagram, table row, code block, or exercise prompt that becomes unclear. Put
public book path in the command or page field and include one evidence signal
from the page. A no-clone report is useful only when it names a public page and
one visible signal; broad praise or broad confusion is not enough.
This edition is intentionally public before it is final.
Citation, Reuse, And Support
Short version:
- The public book will always remain open access at hghalebi.github.io/category_theory_transformer_rs.
- The source repository is available at github.com/hghalebi/category_theory_transformer_rs.
- Cite both the public book URL and the source repository URL where reuse is allowed.
- Personal and individual study are allowed with clear citation.
- One reader may study, cite, link, clone, and run the project for personal learning.
- Commercial or organizational group reuse involving more than one person requires written permission before reproducing, adapting, distributing, or teaching material from the book or repository beyond short quotation, linking, review, and individual-study allowances. This includes company workshops, internal team workshops, classes, cohorts, courses, and training programs.
- Company workshops, internal team workshops, paid workshops, commercial training programs, course packs, adapted slide decks, handouts, labs, and workshop packets require written permission when they reuse substantial material from this project.
- When Kindle or hard copy editions are available, buying the Kindle version or a hard copy supports continued public work. Paid editions are support editions, not access gates.
The public book will always remain open access at hghalebi.github.io/category_theory_transformer_rs.
The source repository is available at github.com/hghalebi/category_theory_transformer_rs.
These are custom citation-and-permission terms. Open access means the public book remains free to read online; it does not mean unrestricted commercial redistribution, commercial training use, company workshop use, or organizational group reuse.
The source code is published so readers can inspect, run, test, and contribute to the examples. Substantial reproduced code, prose, exercises, diagrams, or adapted teaching material used in a commercial or organizational group setting follows the same written-permission rule.
Original material from this book may be linked, quoted in short form, reviewed, or discussed for personal, academic, or noncommercial educational use when the source is clearly referenced and both coauthors are credited.
Suggested reference format. Use both the public book URL and the source repository URL:
Ghalebi, H., & Jafarranmani, F.
Category Theory for Tiny ML in Rust.
Open-access working draft.
Book: https://hghalebi.github.io/category_theory_transformer_rs/
Source: https://github.com/hghalebi/category_theory_transformer_rs
Use this citation in permitted reuse contexts such as papers, posts, slides, course notes, workshop pages, repositories, and public references.
Keep both URLs in public references. The book page is the open-access reading surface; the source repository is the executable Rust source for the examples.
Reproducing, adapting, distributing, or teaching material from this book or repository in a commercial or organizational setting that involves more than one person requires written permission from the project owners, except for short quotation, linking, review, and individual-study allowances. This includes company workshops, company-sponsored workshops, internal team workshops, company reading groups based on copied or adapted material, paid training material, course packs, adapted slide decks, handouts, labs, and workshop packets. If the material is reused by or for a company, team, class, workshop, cohort, course, or training program with more than one person, request written permission first. Citation is required where reuse is allowed, but citation alone is not permission and does not replace written permission for commercial or organizational group reuse. See the repository license and reuse terms.
Company workshops, internal team workshops, paid workshops, and workshop packets count as commercial or organizational group reuse when they reproduce, adapt, distribute, or teach substantial material from the book or repository.
Permission requests should start through the source repository, for example by opening an issue or contacting the maintainers from the repository page.
Opening an issue or sending a request does not itself grant permission. Only an explicit written approval from a project owner or maintainer grants permission for the requested commercial or organizational group use.
When Kindle or hard copy editions are available, buying the Kindle version or a hard copy is a way to support continued work on the project. Paid editions are support editions, not access gates. Paid editions will not remove free public access to the online book.
External works cited or referenced by this book remain under their own licenses, terms, and attribution requirements.