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.

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

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.

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 understand what you're building and why it fails.

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.

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.

Most books about ChatGPT explain the magic. This one shows you the math. Inside Large Language Models, Volume I takes a curious beginner from "what is an LLM" to a complete, trained GPT, with nothing more than high-school algebra, a working laptop, and a willingness to read carefully. Every formula is walked through by hand. Every line of code comes with a plain-English explanation. By the end you will have built, trained, and run your own transformer from scratch, and you will know exactly what is happening inside. No PhD or Data Science required. No prior machine learning needed. Just curiosity and a calculator.

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

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.

I wanted to understand how ChatGPT and other large language models (LLMs) really work, so I read a lot of books, watched YouTube videos, asked hundreds of questions, and wrote it all down. This book is the result. If you want to understand how large language models like ChatGPT actually work, from tokens and vectors to transformers and training, this book will explain it in a clear, approachable way.

Most AI systems fail in production. They chain prompts together and call it "agentic". They collapse under real-world pressure. The gap isn't the technology – it's missing infrastructure. This book bridges that gap. Learn the architecture patterns and infrastructure to build autonomous AI systems that work at scale. Master what distinguishes real agents from chatbots with tools. Deploy with confidence. Stop building demos. Start shipping production agentic AI.

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.

This edition includes a deeper exploration of machine learning and natural language processing, which I am excited to share. I have added new chapters on nonlinear models, multivariate techniques, and text analysis. You will find implementations of algorithms like Support Vector Machines, Neural Networks, and Principal Component Analysis, all using Rust's powerful crates such as smartcore, linfa, and tch. These examples prove that Rust is the ideal tool for complex data analysis tasks.

"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