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 GraphRAG: Building an Intelligent Research Assistant with Knowledge Graphs English | June 2, 2026 | ASIN: B0H3QXVSY4 | 804 pages | Epub | 9.09 MB Build a Complete AI Research Assistant That Runs 100% Locally - No API Costs, No Cloud, No SubscriptionsWhat if your documents could answer questions about themselves - not just find keywords, but explain how ideas connect across hundreds of pages?Traditional search finds documents. Traditional RAG retrieves paragraphs. But neither can answer: "How is Dr. Martinez connected to the CRISPR-Adapt project through her collaborators?" - because that answer lives across multiple documents and requires understanding relationships.GraphRAG changes that.This book teaches you to build a complete Graph-based Retrieval Augmented Generation system from scratch. You will transform unstructured documents (PDFs, Word files, text) into a queryable knowledge graph, then answer complex multi-hop questions by combining vector search with graph-based reasoning.Everything runs locally on your machine. Zero cloud costs. Zero API fees. Your data never leaves your computer.What you will build across 11 hands-on chapters:* A document processing pipeline that extracts entities and relationships using a local LLM* A Neo4j knowledge graph with vector embeddings for hybrid search* Two query modes - LLM-First for flexible exploration and Graph-First for precise, low-hallucination answers* A contradiction-handling system that stores all versions of facts transparently* A web interface that makes your system accessible to non-technical users* A production-ready pipeline tested with 1,000+ documentsThe tech stack: Python, Neo4j, Ollama, Qwen2.5, and nomic-embed-text - all free, all open-source.This book is for you if:* You are a software developer, data engineer, or AI practitioner* You can write intermediate Python and use the Linux command line* You have been frustrated by AI systems that find documents but cannot explain connections* You want to build real AI systems, not just call cloud APIsNo prior experience with graph databases, Neo4j, or LLMs is required. This book teaches you everything from installation to production deployment.By the end, you will have a working system that processes thousands of documents and answers complex questions with citations, reasoning paths, and 98% accuracy - all running on your own hardware at $0/month.  Graph Machine Learning Mastery: A Complete Guide to Graph Neural Networks, Graph Transformers, Temporal GNNs, and LLM-Powered Graph AI with PyTorch Geometric & DGL English | December 17, 2025 | ASIN: B0G7P5HV52 | 346 pages | Epub | 887.07 KB Graph Machine Learning MasteryA Complete Guide to Graph Neural Networks, Graph Transformers, Temporal GNNs, and LLM-Powered Graph AI with PyTorch Geometric & DGLGraph-structured data powers today's most advanced AI systems-from recommendation engines and fraud detection to drug discovery, cybersecurity, and large-scale knowledge graphs. Graph Machine Learning Mastery is the definitive, end-to-end guide for engineers, researchers, and data scientists who want to design, train, scale, and deploy production-ready graph AI systems using state-of-the-art techniques.This book goes far beyond theory. You'll master Graph Neural Networks (GNNs), Graph Transformers, Temporal & Dynamic Graph Models, and LLM-augmented Graph AI, all with hands-on implementations using industry-standard frameworks like and .What You'll LearnBuild powerful GNN architectures: GCN, GAT, GraphSAGE, GIN, heterogeneous and large-scale GNNsTransition from GNNs to Graph Transformers with positional encodings and attention mechanismsModel temporal and dynamic graphs using TGN, TGAT, DySAT, and continuous-time message passingDesign LLM + GNN hybrid systems for reasoning, knowledge graphs, and GraphRAG pipelinesApply graph ML to real-world domains: fraud detection, recommender systems, molecular graphs, finance, telecom, and cybersecurityTrain, optimize, monitor, and deploy graph models in production environmentsIntegrate GNNs with graph databases, MLOps pipelines, and scalable inference system.Hands-On, End-to-End ProjectsYou'll implement complete production-grade projects including:Node classification, graph classification, and link predictionTemporal graph forecastingMolecular property prediction with OGB benchmarksGraph-augmented LLM systems for intelligent reasoning and recommendation.Each project walks you through data preprocessing, model architecture, training, evaluation, deployment, and monitoring-so you don't just learn concepts, you build real systems.Who This Book Is ForData scientists and ML engineers expanding into graph-based AIAI researchers exploring next-generation GNN and Transformer architecturesBackend and platform engineers deploying graph intelligence at scaleProfessionals working with knowledge graphs, recommendation systems, and complex networksA working knowledge of Python and basic machine learning is recommended.Why This Book Stands OutUnlike fragmented tutorials or outdated references, Graph Machine Learning Mastery delivers a modern, unified, and production-focused roadmap-from classical graph learning to cutting-edge LLM-powered Graph AI. With deep technical insight, real-world case studies, and extensive appendices packed with APIs, cheat sheets, troubleshooting guides, and learning paths, this book is designed to become your long-term reference and career accelerator.If you're serious about mastering Graph Machine Learning, Graph Transformers, Temporal GNNs, and LLM-driven AI systems, this is the book you've been waiting for.  Graph AI Systems Engineering: A Complete Guide to GraphRAG and Graph Machine Learning forScalable AI Systems with Python and PyTorch Geometric English | May 2, 2026 | ASIN: B0GZCR72K1 | 322 pages | Epub | 7.71 MB Graph AI Systems EngineeringA Complete Guide to GraphRAG, Graph Machine Learning, Knowledge Graphs, Graph Neural Networks, and LLM-Powered AI Systems with PyTorch Geometric, DGL, and Real-World Production ArchitecturesWhat if the most valuable signal in your data isn't the data itself-but the relationships between it?Traditional machine learning treats observations as isolated rows in a table. But real-world systems don't work that way. Fraud spreads through transaction networks. Recommendations emerge from patterns of user behavior. Supply chains, cybersecurity systems, financial markets, social platforms, molecular biology, and modern AI applications are fundamentally relational systems.Graph AI changes how machines understand these systems.In Graph AI Systems Engineering, you'll learn how to design, build, train, scale, and deploy production-grade graph intelligence systems using Graph Machine Learning, Knowledge Graphs, Graph Neural Networks (GNNs), Graph Transformers, GraphRAG pipelines, and LLM-powered architectures.This practical, engineering-focused guide is designed for developers, ML engineers, AI architects, data scientists, and technical professionals who want to move beyond isolated models and build intelligent systems that reason over relationships, structure, and context.Inside this book, you'll learn how to:Build graph data pipelines using PyTorch Geometric (PyG), DGL, and NetworkXDesign and train Graph Neural Networks including GCNs, GraphSAGE, GATs, GINs, and Relational GNNsWork with temporal and dynamic graphs for evolving real-world systemsCreate enterprise-grade knowledge graphs and GraphRAG pipelinesCombine LLMs with graph machine learning for semantic search, recommendation systems, and AI copilotsApply graph AI to fraud detection, healthcare, cybersecurity, logistics, NLP, and recommendation enginesScale graph AI systems using distributed training, neighborhood sampling, graph partitioning, and GPU accelerationDeploy production-ready graph applications with monitoring, inference pipelines, and MLOps workflowsGraphUnlike fragmented tutorials or shallow introductions, this book emphasizes both conceptual clarity and practical implementation. Every major topic is reinforced with visual explanations, architecture diagrams, mathematical intuition, code walkthroughs, troubleshooting guidance, production notes, and hands-on projects.You'll explore how companies like Google, LinkedIn, Pinterest, YouTube, Spotify, and major financial institutions use graph AI to power recommendation engines, fraud detection systems, ranking algorithms, and large-scale knowledge platforms.Inside the book, you'll build systems including:Fraud detection pipelines using transaction graphsGraph-powered recommendation systemsKnowledge graph reasoning applicationsTemporal graph forecasting modelsMolecular property prediction systemsLLM + GraphRAG hybrid architecturesEnterprise-scale graph ML workflowsProduction-ready graph inference pipelinesThe book follows a progressive roadmap:Foundation Track - Learn the core principles visually and intuitivelyPractitioner Track - Build graph ML systems step by stepProduction Track - Scale and deploy graph AI in real-world environmentsWhether you're an AI engineer, backend developer, data scientist, or researcher exploring next-generation AI systems, this book gives you the practical skills and architectural understanding needed to master Graph AI.The future of AI is not just language. It is relationships, structure, reasoning, and connected intelligence-and graphs are the language that makes it possible.  Grammar 101: From Split Infinitives to Dangling Participles, an Essential Guide to Understanding Grammar (Adams 101 Series) English | May 9, 2017 | ASIN: B01MSBQFOJ | 289 pages | Epub | 1.76 MB Learn the rules of the English language with this easy and engaging style guide.Grammar is complicated. But many textbooks and style guides are often so long and tedious that even English teachers use them as a classroom doorstop. Grammar 101 is a fun, comprehensive guide focusing on the essentials of grammar. With simple explanations and entertaining examples, you can learn everything you need to know to employ commas, semicolons, and em dashes with confidence. You'll even be prepared to wrangle your run-on sentences and sharpen your syntax with ease. So whether you're looking for an introduction to the rules of grammar or a refresher to keep on your desk, Grammar 101 has all the answers.  Gradient Cascade Physics: A Unified Framework for Vacuum Structure, Nuclear Geometry, and Emergent Temporal Form English | April 26, 2026 | ASIN: B0GYKWC748 | 168 pages | Epub | 355.70 KB An interpretive framework for thinking systematically about vacuum, gravity, and the structure of matter - proposed honestly with its epistemic status drawn explicitly. This book develops a framework in which three apparently disparate physical phenomena - vacuum pumping, gravitational binding, and osmotic flow - are recognized as instances of a common mathematical structure: the maintained multistage potential gradient. From this foundation comes a systematic reinterpretation of atomic structure in which nuclei function as geometrically-organized vacuum extrema, and electronic orbitals carry the system's clock information. The framework rests entirely on standard physics. The mathematical machinery is drawn from quantum electrodynamics, general relativity, and statistical mechanics. What is novel is the systematic application of cascade analysis across substrates - and the observation that this cross-substrate application generates testable predictions that single-substrate approaches do not. The book draws on the lineage of the Page-Wootters mechanism, shape dynamics, relational quantum mechanics, the Wheeler geometrodynamic program, and the constructal theory of Bejan, assembling existing pieces into an engineering-oriented synthesis. The book is structured as twenty-two chapters across six Parts. Part I establishes foundations: the third design paradigm of gradient configuration, the mathematics of potentials and flows, a survey of vacuum physics, and the problem of time. Part II develops the formal cascade framework. Part III takes up the speculative core - Hypothesis H-T (Electronic Temporal Primacy) and Conjecture 8.1 (the Nuclear Cascade Hypothesis). Part IV derives specific testable predictions, centered on the differential-clock experiment that distinguishes nuclear from electronic atomic clocks under environmental perturbations. Part V applies the framework to engineering: Casimir cavity cascades, f-orbital computation, gradient-maintained exotic matter, and vacuum-gradient fusion confinement. Part VI addresses biology, philosophy, and what the framework does not predict. The book is honest about what it is. The opening chapter "On the Framework's Epistemic Status" explicitly classifies the book's content into three tiers: established empirical physics, the cascade reframing as one productive interpretive lens among others, and specific conjectures that experiments can falsify. The framework is offered for collective engagement, not asserted as a finished result. Mathematical prerequisites are modest: multivariable calculus, basic linear algebra, introductory quantum mechanics. The intended audience is working physicists and engineers, with annotated bibliography directing readers to specialized literature for each chapter.  Google ImageFX meistern Deutsch | April 16, 2026 | ASIN: B0GX2SZPVK | 142 Seiten | Epub | 23.28 MB Erstellen Sie atemberaubende Bilder - ganz ohne Design-Studium. Google ImageFX meistern: Der Leitfaden zur KI-Bildgenerierung für Nicht-Designer ist Ihr Schnelleinstieg, um aus einfachen Worten fesselnde Kunstwerke zu erschaffen.Geschrieben für Kreative, Marketing-Profis, Self-Publisher und Visionäre des Alltags - dieser unterhaltsame Schritt-für-Schritt-Ratgeber entmystifiziert Googles leistungsstarke Imagen-2-Engine und vermittelt Ihnen die Profi-Techniken für perfekte Prompts - ganz ohne Fachjargon.Im Buch entdecken Sie:Prompt-Schreiben Grundlagen - glasklare Formeln für Motive, Stile, Beleuchtung und Stimmung, die immer funktionieren.Problemlöser-Toolkit - neun häufige Anfängerfehler (und wie Sie sie exakt vermeiden) mit direkten Bildvergleichen.Expressive Chips beherrschen - nutzen Sie ImageFX' Ein-Klick-Variationen, um Ideen im Handumdrehen neu zu kombinieren.Konsistenz bei Figuren und Szenen - halten Sie Ihre Hauptfigur über einen ganzen Comic, Kurs oder eine Werbekampagne hinweg einheitlich.Stilistische Spickzettel - über 150 handverlesene Modifikatoren (Beleuchtung, Kamerawinkel, künstlerische Medien, Stimmungen), um jeden Stil perfekt umzusetzen - von fotorealistisch bis Salvador-Dalí-surreal.Fortgeschrittene Techniken - Fokuspunkt-Kontrolle, Workarounds für Negativ-Prompts, VPN-Tipps für Geosperren und mehr.Praxisnahe Fallstudien - echte Probleme direkt aus Reddit, X und Discord - mit getesteten Lösungen.SEO-Turbo-Boost - Tipps für Benennung, Alt-Text und Tagging, damit Ihre KI-Bilder auf Pinterest, Behance und DeviantArt gefunden werden.Jedes Kapitel ist vollgepackt mit Prompts zum Kopieren und Einfügen, schnellen Erfolgen und lebhaftem Humor - so bleiben Sie beim Lernen motiviert.Schluss mit dem Starren auf ein leeres Prompt-Feld. Öffnen Sie diesen Ratgeber, tippen Sie ein paar Worte ein, und sehen Sie zu, wie Google ImageFX Ihre Vorstellungen zum Leben erweckt.Bereit, Bilder zu erschaffen, die so mutig sind wie Ihre Ideen? Scrollen Sie nach oben und sichern Sie sich jetzt Ihr Exemplar.  Good Lime: Karibisch & vegetarisch kochen Deutsch | 29 Aug. 2022 | ISBN: 3710606330 | 208 Seiten | EPUB (True) | 26.35 MB Hol dir den Geschmack und den Spirit der Karibik in dein ZuhauseGOOD LIME: Raus aus dem hektischen Alltag, rein in lässige Freiheit und das Leben in vollen Zügen mit karibischen und vegetarischen Rezepten, kühlem Bier, anregenden Gesprächen und guter Musik genießen. Lassen wir uns ein auf das Lebensgefühl der Karibik und machen aus spontanen Treffen mit Freunden und Familie einen unvergesslichen Tag oder Abend. Und schon sind wir mittendrin!Mit einfachen Rezepten aus der karibischen KücheCrunchy KichererbsenKochbananen-ChipsSpinat-ChokaManiok-Kürbis-KuchenGebratene Okra-Schotenund viele weitere bunte und geschmacksintensive SpeisenMehr als ein Kochbuch: Über das Suchen und Finden der eigenen WurzelnIn GOOD LIME teilt Beni Tonka mit uns seine Geschichte zwischen Südhessen und der Karibik. Er erzählt von dem Abenteuer, wie er seinen Vater und seine ganze Familie in Trinidad und Tobago mit Mitte 20 vor allem über das Essen und die Lebensart kennen und lieben lernte. Und schenkt uns ein Koch- und Lesebuch mit den besten vegetarischen Rezepten seiner deutschen Großmutter und seiner neuen karibischen Heimat.Mit einem Vorwort von Alice HastersHol dir die bunte karibische Leichtigkeit in dein Zuhause: mit Playlist zum BuchDrinks, Music & Time - that's Good Lime!  GitHub Copilot and AI Coding Agents: AI-Assisted Software Development, Prompt Engineering, Automated Workflows, and Multi-Agent Systems with LLMs (DevTech series Book 14) English | May 31, 2026 | ASIN: B0H275M9RP | 347 pages | Epub | 487.43 KB Have you ever wondered why some developers seem to build faster, ship better code, and automate tasks that still consume hours of your day? What if the next breakthrough in software engineering isn't another programming language or framework-but the ability to collaborate effectively with AI?What exactly is GitHub Copilot capable of? Is it simply an advanced autocomplete tool, or is it the beginning of a new era where intelligent coding agents become active partners in software development? How do today's most productive developers use AI to accelerate coding, debugging, testing, documentation, and deployment without sacrificing quality or control?The future of software development is no longer about coding alone-it's about orchestrating intelligence.GitHub Copilot and AI Coding Agents takes you deep into the rapidly evolving world of AI-assisted software engineering, where large language models, intelligent agents, automated workflows, and prompt-driven development are transforming how modern applications are built.Have you struggled to write better prompts that generate more accurate code? Have you wondered how AI agents can collaborate with one another to solve complex development challenges? Are you curious about how multi-agent systems work, how they communicate, and how they can automate entire software workflows from planning and coding to testing and deployment?This book answers those questions-and many more.You'll explore how GitHub Copilot is reshaping developer productivity, discover the principles behind effective prompt engineering, and learn how to harness AI tools to reduce repetitive work while increasing creativity and efficiency. You'll gain practical insights into AI-assisted coding, intelligent automation, agent orchestration, workflow optimization, and the growing ecosystem of LLM-powered development tools that are redefining the profession.What if you could delegate routine engineering tasks to AI and focus on solving higher-value problems?What if multiple AI agents could collaborate together to accelerate development cycles and improve software quality?What if you could design intelligent systems that not only generate code, but also review, test, refine, and optimize it?As AI capabilities continue to advance, understanding how to work alongside these technologies is becoming one of the most valuable skills a developer can possess. Whether you're a software engineer, architect, technology leader, DevOps professional, engineering student, startup founder, or AI enthusiast, this book provides the knowledge and perspective needed to navigate the next generation of software development.The question is no longer whether AI will influence software engineering.The real question is: will you know how to leverage it effectively while others struggle to keep up?If you're ready to unlock the full potential of GitHub Copilot, master AI-assisted development, build intelligent coding workflows, and explore the power of multi-agent systems powered by large language models, then this book is your guide.Scroll up and get your copy today to start building software faster, smarter, and with the power of AI at your side.  GitHub Copilot Modernization Handbook: Discover, document, and upgrade legacy code while guarding business rules and uptime. English | May 11, 2026 | ASIN: B0GX3B4VJD | 243 pages | Epub | 5.68 MB Still maintaining systems that nobody fully understands?You push updates carefully, hoping nothing breaks. Releases take longer than they should. Business rules live in hidden corners of the codebase. One wrong change could impact uptime, compliance, or revenue. If that sounds familiar, you're not alone and you're not stuck.GitHub Copilot Modernization Handbook shows you how to take control of legacy systems without risking the stability your business depends on. This book presents a structured, real-world approach to discovering, documenting, and upgrading complex codebases while preserving the logic that keeps them running.Instead of risky rewrites or guesswork, you'll learn how to modernize step by step using AI-assisted workflows, controlled refactoring patterns, and production-safe deployment strategies. Every concept focuses on execution in real environments, not theory.What you'll gain from this book:A clear method to assess and map legacy systems before making changesProven techniques to extract and protect critical business rulesPractical workflows for refactoring without breaking production behaviorStrategies to integrate GitHub Copilot into real engineering processesApproaches for building safety nets with tests and validation pipelinesDeployment practices that reduce risk and improve release confidenceModernization patterns used in banking, public sector, and healthcare systemsA structured way to measure progress using real KPIs that matterYou'll also learn how to coordinate changes across multiple repositories, manage dependencies at scale, and maintain uptime while transforming systems that were never designed to change quickly.If you're an engineer, architect, or technical leader responsible for keeping systems running while moving them forward, this handbook gives you the clarity and structure you need to act with confidence.Ready to modernize without breaking what matters? Get your copy today and start turning complex legacy systems into manageable, scalable platforms.  GitHub Copilot Agents with Visual Studio 2026 Made Easy: Debug, Test, Profile, Refactor, and Modernize C# and .NET Applications with AI-Assisted Development English | May 12, 2026 | ASIN: B0GZL54CZS | 614 pages | Epub | 697.48 KB GitHub Copilot Agents with Visual Studio 2026 Made Easy is a practical beginner-to-intermediate guide for developers, students, educators, and .NET professionals who want to use AI-assisted development effectively inside Visual Studio 2026.As programming tools become more intelligent, developers are no longer limited to writing code manually line by line. With GitHub Copilot and agent-style workflows, you can explain unfamiliar code, debug errors, generate unit tests, improve performance, refactor messy methods, review security risks, modernize older applications, and build complete C# projects more efficiently.This book teaches you how to use Copilot as a development assistant, not as a replacement for understanding. You will learn how to write better prompts, review AI-generated code, test suggestions, and make responsible programming decisions.Inside this book, you will learn how to:Use GitHub Copilot effectively in Visual Studio 2026Understand Copilot Chat, inline suggestions, and agent-style workflowsWrite better prompts for coding, debugging, testing, and refactoringUse Copilot to explain existing C# codeFix common C# errors with AI assistanceGenerate unit tests with xUnit and MSTestImprove code quality, naming, validation, and maintainabilityOptimize LINQ and Entity Framework queriesModernize older .NET and Windows Forms applicationsUse Copilot with ASP.NET Core APIsReview code for security, validation, sensitive data exposure, and error handlingCreate custom Copilot agent-style workflowsUse Copilot responsibly in education and professional developmentBuild a complete AI-assisted C# Sales Order Calculator projectThis book is written in a clear, step-by-step style with practical examples, prompts, code samples, mini projects, chapter summaries, and exercises. It is especially suitable for readers who want to learn how AI can support real programming work while still building strong software development fundamentals.Whether you are a student learning C#, an educator teaching programming, a Windows Forms developer maintaining older applications, an ASP.NET Core developer building APIs, or a professional .NET programmer exploring AI-assisted workflows, this book will help you become more productive, more confident, and more systematic in modern software development.Learn how to debug, test, profile, refactor, modernize, and build better C# and .NET applications with GitHub Copilot Agents and Visual Studio 2026. |
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