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.
A model that crashes is a good day. The dangerous failures return answers — plausible, fluent, and wrong. 100 failures. 63 diagnostic instruments. One rule: inspect what actually happened.
- Discrete Mathematics for Computer ScienceAlexander S. Kulikov, Alexander Golovnev, Alexander Shen, Vladimir Podolskii, and Marie Brodsky
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.
- Claude Code MasterclassBuild Real-World Software with Claude Code, AI Workflows, and Hands-On ProjectsLuca Berton
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.
- Applied Conformal Prediction:Practical Uncertainty Quantification for Real-World MLPractical Uncertainty Quantification for Real-World ML Learn Conformal Prediction (CP), the state-of-the-art technique for building statistically valid, model-agnostic prediction intervalsValery Manokhin
A powerful new book on Conformal Prediction by bestselling author and machine learning expert Valery Manokhin, bridging theory and real-world machine learning. Discover how to quantify uncertainty with statistical guarantees—across deep learning, time series, forecasting, and more. Preorder now before the price goes up.
- Running Local LLMs on Your Own HardwareA Practical Guide to Private, Offline, and Self-Hosted Large Language ModelsYohan Rodriguez
A hands-on guide to downloading, running, serving, and maintaining open-weight LLMs on your own machine (492 manuscript pages).
- Production-Grade Python, LangChain & LangGraphA deployment-first handbook for developers who know Python syntax but have not shipped PythonFiodar Sazanavets
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.
- My Adventures with Large Language ModelsBuild foundational LLMs from Transformers to DeepSeek, from scratch, in PyTorch.Prathamesh S.
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.
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.
- Local AI Engineering with OllamaRun, understand, customize, fine-tune, and build agentic apps on your own hardwareAymen El Amri
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.
- Einführung in die GIS-ProgrammierungEin praktischer Python-Leitfaden für Open-Source-Geoinformatik-ToolsQiusheng Wu
Entfesseln Sie die Kraft geospatialer Daten mit Python! Dieser praxisorientierte Leitfaden richtet sich an Einsteigerinnen und fortgeschrittene Nutzerinnen, die räumliche Analyse und interaktive Kartierung mit Open-Source-Tools erkunden möchten. Sie lernen anhand praxisnaher Beispiele mit realen Daten und erwerben Fähigkeiten in Python-Programmierung, Vektor- und Rasteranalyse, Webkartierung und Cloud-Computing. Egal, ob Sie Studentin, Forscherin, GIS-Fachkraft oder Datenwissenschaftler*in sind – dieses Buch gibt Ihnen die Werkzeuge an die Hand, um geoinformatische Herausforderungen souverän zu meistern.
Multitasking with Micropython. Cooperative Scheduling.
- Private AI with SparkDesign, package, and operate private AI locally using Apache Spark, batch pipelines, and vLLM accelerationGitforGits | Asian Publishing House
For those who want to build controlled, reproducible AI systems entirely within their own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.
Anyone can run INT4 and read off the accuracy drop. This book explains why that number is what it is — building every quantization method from scratch, breaking it on purpose, and measuring the result. Quantization, from the bits up.
- Practical Python for Production under PressureManaging the insanity that is developing pipelines alongside a production at full speedAlex Telford
Facing the insanity that is pipeline development alongside a production in full swing, "Practical Python for Production under Pressure" helps TDs to deliver quality tools and pipelines amidst the chaos.This book focuses on practical workflows and theory to build practical tools and pipelines within an ever changing environment using python, PySide and duct tape ingenuity. Learn about managing communications, building pipelines, working with AI in production, user experience and more.
