Books

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.

STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use AI models. This book breaks down the architecture of real AI applications, like an AI-powered code editor or a smart learning app. It gives you a deep, practical look at the real-world challenges and solutions for building these systems. It discusses system design concepts for systems that use LLMs.

The hardest part of building AI is not the model. It is everything around it. The Art of Harness Engineering is a practical guide to turning AI prototypes into reliable products. It covers testing, guardrails, observability and governance, giving you the tools to build AI systems people can trust and organizations can run with confidence.

"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

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.

Build Claude into a reliable specialist for your domain. The Claude Skills Handbook takes you from the first idea through architecture, implementation, testing, security, deployment and monitoring, with practical patterns and working examples throughout. Learn how to build Skills that scale, stay maintainable and work in the real world.

MCP can transform the way you work with Claude Code. This practical guide takes you from the basics to production, showing you how to connect tools, build integrations, automate workflows and ship reliable, secure solutions you can use right away.

Bestselling book on building LLMs. A practical, project-driven manual for engineers who want to understand how modern language models are built — and where they fail — by writing every layer themselves. From a scalar autograd engine to RLHF to fused specialists, in 36 hands-on projects with deliberate sabotage experiments. Build it. Break it. Measure it.

AI is changing the way we find and fix software vulnerabilities. Intelligent Discovery explores the techniques behind this shift, from static analysis to AI-driven vulnerability research and automated repair. With a clear-eyed look at both the potential and the pitfalls, it shows what these tools can do, where they fall short and why human expertise still matters.

Network traffic can look like noise until you know what to look for. This practical guide shows you how to investigate unfamiliar protocols, analyze packet captures and build reliable workflows with Claude Code. Learn to reverse engineer traffic, test your assumptions and turn raw network data into findings you can trust.

Build production-grade RAG systems in C# — from an 80-line Hello World to a fully deployed Azure pipeline with the Microsoft Agent Framework, MCP, GraphRAG, multi-agent orchestration, eval gates, and EU AI Act-ready audit trails. 682 pages, 25 chapters, one evolving enterprise project, every line of code runnable in .NET 10.

Büyük dil modellerine dair ana kavramları ve pratik uygulamaları kapsayan, açık ve görsellerle desteklenmiş bir rehber. Projeler, mülakatlar ve kişisel öğrenme için idealdir.

You don’t need massive compute or big-tech resources to build real AI. This hands-on guide shows you how to build and fine-tune your own small language model—from scratch—using accessible tools like Google Colab. Learn transformers step-by-step, train and align models on your own data, and deploy practical AI systems that run on consumer GPUs. If you’ve ever wondered “Can I build my own model?”—this book proves the answer is yes.

Your AI builder made an app that works. This book shows you whether it's also leaking your API keys, your users' data or your source code, and gives you copy-paste prompts so the same AI can fix it before a stranger finds it.

Your use cases have a new reader: an AI agent that turns them directly into code and tests. Implementation is now cheap but expressing intent precisely is the bottleneck. This guide teaches you to write use cases that work for stakeholders, engineers, and the AI agents that implement from them.