Books

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.

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.

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

Build a complete LLM inference engine in C++ — from a blank project to a working Transformer that loads a real model and generates text. Forged one challenge at a time, with tests that prove every piece works before you move on.

Pull a model onto a machine you own, shape it with a Modelfile, fine-tune your own adapter, and build a chat app that calls tools and talks to an MCP server, all running on your own hardware. By the end, you'll know exactly where owning your AI beats renting it, and where it doesn't.

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

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.

PyTorch Deep Dive is a practical guide to mastering modern deep learning with PyTorch. From core concepts to advanced topics like transformers, diffusion models, and production deployment, it combines clear explanations, hands-on examples, and real-world best practices to help you build and scale AI applications with confidence.

Transform your software creation process with AI Driven Development (AIDD). Learn to harness AI to generate programs, unlocking 10x - 20x productivity gains while building components, tests, and documentation. Written for software engineers, product managers, code conjurers and aspiring tinkerers, this book teaches you to express complex functional requirements using natural language with the precision of code, utilizing SudoLang - a language designed specifically for communicating ideas to AI language models. You'll learn to streamline your workflow, reduce code complexity, and craft more intelligent, responsive applications that adapt to user needs.Design and implement AI-powered applications using SudoLang, and any common programming language or framework. Examples will use JavaScript, Next.js, and React.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

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).