Python

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

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.

This book is not just a Python course with a networking flavour or a networking course with a few scripts attached. You already know how routing converges, why VLANs leak, and what a half-open TCP session looks like on the wire. What this book adds is the ability to transform that knowledge into code that runs across hundreds of devices, reports accurately on its findings and never makes changes that cannot be undone.

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.

PyTorch Deep Dive is a practical guide to mastering modern deep learning with PyTorch. From core concepts to advanced topics like transformers, diffusion models, and production deployment, it combines clear explanations, hands-on examples, and real-world best practices to help you build and scale AI applications with confidence.

The new edition is revised and updated with Python 3.10 along with new chapters on AsyncIO and Docker containers. The book will provide examples and guidance to manage your network, improve reliability, and simplify your task. The fourth edition also includes network context examples of Ansible, Flask, Elasticsearch, Git, GitLab and more.

Explore recipes for both classical quantitative finance approaches to modeling financial data, as well as modern machine learning and deep learning solutions

卫星每天获取海量影像数据，但将像素转化为洞察需要借助人工智能。本书将教你如何使用 Python 和开源工具构建、训练并应用深度学习模型，处理真实的卫星影像。全书包含 23 个可运行的代码章节，帮助你即学即用。所有代码示例均可在 https://book.opengeoai.org 免费获取。

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.

Unlock the power of geospatial data with Python! This hands-on guide is designed for beginners and intermediate users eager to explore spatial analysis and interactive mapping using open-source tools. You'll learn how to work with real-world data through practical examples and build skills in Python programming, vector and raster analysis, web mapping, and cloud computing. Whether you're a student, researcher, GIS professional, or data scientist, this book will equip you with the tools to tackle geospatial challenges with confidence. Color-print copies are available through Amazon.

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!

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.

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.

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.

Are you a data scientist or analyst struggling to take your Jupyter Notebook prototypes to the next level? Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity? This book is the solution you’ve been seeking. This comprehensive guide bridges the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and collaborative solutions.