Python

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.

800 pages. 11 chapters. The full forecasting stack in Python — from ARIMA to foundation models — with production-grade code and proper evaluation. No hype.

AI engineering is becoming one of the most valuable and in-demand areas of software development, and Python, LangChain, and LangGraph are core skills for building the systems behind it. Learn how to turn basic Python knowledge into production-grade backend and agentic AI applications—and move toward an engineering niche centered on building and controlling AI rather than competing with it.

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.

A hands-on guide to downloading, running, serving, and maintaining open-weight LLMs on your own machine (492 manuscript pages).

Python looks the same on the surface, but almost everything underneath has changed. This book brings you up to speed on Python 3.13/3.14, the free threaded build, uv, and the tools working engineers actually use today, with runnable examples in every chapter.

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.

Learn Polars, the pandas killer for data analysis.

Los satélites capturan enormes volúmenes de imágenes cada día, pero convertir píxeles en conocimiento requiere IA. Este libro te enseña a construir, entrenar y aplicar modelos de aprendizaje profundo a imágenes satelitales reales utilizando Python y herramientas de código abierto, con 23 capítulos de código ejecutable que puedes probar hoy mismo. Todos los ejemplos de código están disponibles gratuitamente en https://book.opengeoai.org.

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.

We'll stick to five libraries, not because more would be a problem, but because keeping it simple shows how well we can organise things. When you download MNIST with just the standard library, you finally see what a dataset loader was hiding. If you write attention as four lines of NumPy before you ever call a PyTorch module, it's no longer a magic process but just plain arithmetic.

Even if you're a total newbie to the world of GPUs, this book will take you from the basics of CPUs to the current world of GPU programming. All you need is some Python experience and a willingness to explore and try the techniques it offers.This book will walk you through the basics of GPU architectures, show you hands-on parallel programming techniques, and give you the know-how to confidently speed up real workloads in data processing, analytics, and engineering.

Discover how to make Python data processing faster, leaner and ready to scale with Polars. Starting with the basics, this practical guide takes you all the way to production-grade pipelines for massive datasets, with clear explanations of how Polars works, why it is fast and how to get the best performance from it.

Most enterprises can deliver fast, but few can adapt at scale.The Cybernetic Enterprise introduces a unifying operating model that embeds AI, feedback loops, and platform thinking into the DNA of your organization. Learn how to build a system that senses change, learns continuously, and transforms disruption into strategic advantage.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!