Books

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Skip the black-box frameworks. Build a production-grade AI coding agent from scratch in pure Python - cloud or local, tested with pytest, all in a single file.

Everything you really need to know in Machine Learning in a hundred pages.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

Keyword search misses meaning. Vector search misses precision. This book shows you how to combine them into production systems that deliver both, with architecture patterns, model selection frameworks, evaluation methodology, and operational guidance grounded in primary research.

A thorough guide for programmers working with Japanese text, covering fundamental issues like tokenization and recent research topics like generating natural language texts. Working examples are accompanied by extensive reference to allow problem solving even without a background in Japanese or Machine Learning.

Launch Price $9.99 Special! — price will increase as I plan to steadily add more chapters over the coming weeks.

Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.

Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.

Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable machine learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods.

From machine learning to natural language processing, this book will guide you through the cutting-edge world of AI-powered data products. You'll learn how to harness the power of AI to create products that are smarter, faster, and more efficient than anything your competitors can dream of.

Unlock the full potential of C++ to revolutionize your AI and machine learning projects with this definitive guide to high-performance computing. Whether you're an experienced developer or an ambitious newcomer, this book is your gateway to mastering the art of optimizing computational efficiency for cutting-edge AI solutions.

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." —Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." —Karolis Urbonas, Head of Machine Learning and Science at Amazon

Helping C# and .NET developers to learn how to do machine learning and become highly sought-after (and well-paid) AI engineers. No prior experience of ML required!