Books

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.

Чому проєкт-цукерочка через рік перетворюється на непідтримуване пекло? Тому що вас ніколи не вчили базі. Ця книга — єдина система координат, яка нарешті збере ваші розрізнені знання про ООП, GRASP, SOLID та GoF в одну цілісну картину.

Learn SysML v2 with the ultimate guide for all skill levels in MBSE. Authored by insiders, it's your key to unlocking the full potential of system modeling and a passport to mastering your MBSE.

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 complete foundation for Statistics, also serving as a foundation for Data Science. Leanpub revenue supports OpenIntro (US-based nonprofit) so we can provide free desk copies to teachers interested in using OpenIntro Statistics in the classroom and expand the project to support free textbooks in other subjects. More resources: openintro.org.

Unlock the power of DuckDB for modern geospatial analytics. This hands-on guide helps GIS professionals master efficient spatial data management, transforming massive real-world datasets into powerful insights using SQL, Python, and DuckDB’s spatial extension. Full-color print edition is available on Amazon.

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

The book covers every topic in the latest CISSP exam syllabus, organized in a format that makes it easy to drill down on specific exam domains and concepts at-a-glance, making it an essential exam resource for anyone who aims to prepare for the exam without wasting time or money.

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.

With more than 1200 microcontrollers, STM32 is probably the most complete ARM Cortex-M platform on the market. This book aims to be the most complete guide around introducing the reader to this exciting MCU portfolio from ST Microelectronics and its official CubeHAL and STM32CubeIDE development environment.

Your AI agent is only as smart as what it remembers. Most developers treat knowledge representation as an afterthought -- a data structure problem, not an architecture problem. They flatten complexity into schemas that can't express relationships, deploy embeddings without grounding, and build agents that degrade under their own reasoning load. This book changes that. **TypeDB for Edge AI Agents** is a practitioner's guide to building knowledge systems that actually work at the scale and complexity your agents demand. TypeDB is built on type theory -- the same mathematics that powers formal verification and programming language safety. For agents, that means you can express constraints that prevent bugs in your knowledge layer, reason about what's possible and what's forbidden, and build memory systems that don't require constant hallucination detection. With the Rust rewrite in TypeDB 3.0, your agents can carry sophisticated knowledge graphs on-device -- no round-trip to a server, no latency, no external dependencies. This book covers everything from the PERA data model and OWL ontologies to promise graphs for multi-agent coordination, giving you the patterns to design knowledge systems that scale without becoming incoherent. Whether you're an ML engineer, AI architect, or backend developer building production agent systems, this book bridges the gap between type theory and working code. You'll learn to design knowledge graphs that don't decay, structure agent memory that doesn't degrade over time, and coordinate multi-agent systems that respect causality and distributed constraints. Stop building agents that confidently hallucinate -- start building agents that reason correctly, remember reliably, and coordinate with certainty.

This book covers every topic in the latest CISM exam syllabus, approaching topics from the ISACA perspective. It's 325+ pages organized in a format that makes it easy to drill down on specific exam domains and concepts at-a-glance, making it an essential exam resource for anyone who aims to prepare for the CISM exam without wasting time or money.

System Design Workbook – Edição 2026 é uma obra que traduz, organiza e conecta os principais fundamentos da engenharia de sistemas distribuídos sob uma perspectiva prática, moderna e orientada à realidade de produção.

This 2026 edition of the best-selling Kubernetes book is fully updated for the latest versions of Kubernetes and the latest industry trends. You won't find a better and more up-to-date book on Kubernetes. Hand-crafted over the past 8 years by best-selling author Nigel Poulton. This book is a masterpiece.

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.