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 understand what you're building and why it fails.

Mastering Modern Time Series Forecasting is your all-in-one guide to building real-world forecasting systems that work — from classical stats to deep learning and beyond. Whether you're modeling retail demand or energy loads, this book gives you the tools, intuition, and code to go from zero to production. You'll cover ARIMA, ML, deep nets, transformers, and even the rise of FTSMs (Foundational Time Series Models). Written by a practitioner who’s built forecasting solutions for multibillion-dollar businesses, this is the hands-on, honest guide every data scientist, analyst, or forecaster needs.

Most people think they are bad at math. In reality, they were never taught arithmetic properly.This book is a modern English edition of Arithmetic by Alexander P. Kiselev—the text that formed the backbone of mathematical education in Russia and USSR for over a century and helped produce generations of exceptionally strong mathematicians, scientists, and engineers.Unlike modern textbooks that prioritise shortcuts, visuals, and lowered expectations, Kiselev builds arithmetic logically, systematically, and rigorously. Every method is explained. Every operation has meaning. Exercises are carefully sequenced to develop real understanding—not rote pattern-following.This book does not promise “easy math”. It promises something better: clarity, confidence, and competence.Whether you are a student, a parent, a tutor, or an adult rebuilding fundamentals, this book will change how you understand arithmetic—and why so much later mathematics suddenly becomes easier.

大規模言語モデル (LLM) の主要な概念から実践的な応用例まで、簡潔に図解されている学習ガイドです。プロジェクトでの利用、面接対策、個人的な学習にも最適です。

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.

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

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

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.

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.

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.

"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

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。