Kick off your book project in 2 hours, get started with GhostAI in 2 hours, or do both! Free live workshops, on Zoom. You’ll leave with a real book project and a clear plan to keep going. Saturday, June 27, 2026.
Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.
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.
Bad data breaks good code. You’ve written Python that works perfectly in testing, only to watch it fail in production because of a malformed API request, a messy CSV, or a missing config value. That’s the hidden cost of Python’s flexibility: without runtime validation, you’re always one bad input away from a crash. Enter Pydantic. This book takes you from the foundations of data validation to real-world applications in APIs, data pipelines, configurations, and machine learning workflows. Along the way, you’ll explore practical techniques, advanced features, and alternatives like Marshmallow, attrs, and dataclasses, so you’ll always know which tool is right for the job. If you’re a Python developer, data engineer, or FastAPI user, this is your roadmap to writing safer, cleaner, and more reliable code.
A thorough guide for programmers working with Japanese text, covering fundamental issues like tokenization and recent research topics like generating natural language texts. Working examples are accompanied by extensive reference to allow problem solving even without a background in Japanese or Machine Learning.
This book supplements the DM for CS Specialization at Coursera and contains many interactive puzzles, autograded quizzes, and code snippets. They are intended to help you to discover important ideas in discrete mathematics on your own. By purchasing the book, you will get all updates of the book free of charge when they are released.
Quantitative finance in Python: a hands-on, interactive look at the QuantLib library through the use of Jupyter notebooks as working examples.
Satellites capture massive volumes of imagery every day, but turning pixels into insight requires AI. This book teaches you to build, train, and apply deep learning models to real satellite imagery using Python and open-source tools, with 23 chapters of executable code you can run today. All code examples are freely availabe at https://book.opengeoai.org.
The book contains the full transcript of Software Diagnostics Services training with 16 hands-on exercises on various topics related to Linux API.
Learn how large language models work by building one from scratch. This hands-on guide walks you from first principles to a working Transformer you understand inside out.
Build a compiler to learn how programming languages work. Use low-level assembly to learn how computers work. Walks through a minimal yet complete compiler. Compiles a static-typed language into x64 ELF executables.Simple interpreter.Bytecode compiler.x64 assembly & instruction encoding.Translate bytecode to x64 code.Generate binary executables.
Launch Price $9.99 Special! — price will increase as I plan to steadily add more chapters over the coming weeks.
Learn Polars, the pandas killer for data analysis.
Откройте потенциал геоданных с помощью Python! Это практическое руководство создано для начинающих и пользователей среднего уровня, стремящихся освоить пространственный анализ и интерактивную картографию с использованием инструментов с открытым исходным кодом. Вы научитесь работать с реальными данными, выполнять практические задания и приобретёте навыки программирования на Python, векторного и растрового анализа, веб-картографии и облачных вычислений. Эта книга даст вам всё необходимое, чтобы уверенно решать задачи пространственного анализа — будь вы студентом, исследователем, ГИС-специалистом или аналитиком данных.
Completely hands-on so that you can start the real work!
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.