Books

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.

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.

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

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

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

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.

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.

A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.

"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

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.

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!

Master the art of AI interaction with 22 proven prompting techniques, real code examples, and production-tested strategies. From the creator of GitHub's most-starred prompt engineering repository (7,100+ stars).

ブラックボックスのフレームワークは不要。純粋なPythonでプロダクションレベルのAIコーディングエージェントをゼロから構築。クラウドでもローカルでも、pytestでテスト済み、すべて1つのファイルに収まります。

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí