Abe Carlson (20 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware - Have you ever wanted to create your own video games but weren't sure where to begin Godot 4 for Game Creators is a practical, beginner-friendly guide designed to help you build a solid foundation in modern game development using one of the world's most powerful open-source game engines. Whether your goal is to create 2D platformers, immersive 3D experiences, or interactive prototypes, this book walks you through the essential skills with clear explanations, hands-on projects, and real-world examples.You don't need previous programming or game development experience. Starting from the fundamentals, you'll learn how to navigate the Godot editor, organize game projects, write clean GDScript code, create engaging gameplay systems, and transform your ideas into fully playable games.Rather than overwhelming you with theory, this book focuses on learning by doing. Every chapter builds upon the previous one, allowing you to gain confidence as you create increasingly sophisticated game mechanics, environments, user interfaces, animations, audio systems, and interactive experiences.Inside this book, you'll learn how to: - Install and configure Godot 4 for game development- Navigate the editor and organize professional game projects- Master scenes, nodes, and reusable game architecture- Write clean and efficient GDScript code- Create interactive 2D and 3D game worlds- Design responsive player controls and camera systems- Build enemies, collectibles, physics interactions, and gameplay mechanics- Create intuitive menus and user interfaces- Add animations, sound effects, music, and visual polish- Save player progress and manage game data- Optimize performance for smoother gameplay- Debug and test your projects effectively- Export and publish games for desktop, web, and mobile platforms- Apply game development best practices used by independent developersThroughout the book, you'll complete practical projects that reinforce each new concept while building the confidence to create original games of your own. By the end, you'll understand not only how Godot works, but also the design principles and development workflows that professional game creators use every day.Whether you're a student, hobbyist, aspiring indie developer, educator, or someone exploring a new creative career, this book provides the knowledge and practical experience needed to begin your game development journey with confidence.If you're ready to stop dreaming about making games and start building them, Godot 4 for Game Creators is the perfect place to begin.…

Taschenbuch. Condition: Neu. Neuware - Large language models can generate powerful responses, but they do not automatically have access to the knowledge your applications need. Traditional Retrieval-Augmented Generation (RAG) improves this by connecting LLMs to external information, yet many complex questions require more than finding similar text. They require understanding entities, relationships, context, and connected information.Building Intelligent AI with GraphRAG explores how knowledge graphs and GraphRAG can transform retrieval into a more connected and knowledge-aware process.Starting with the foundations of knowledge graphs, this book explains entities, relationships, properties, schemas, graph databases, and knowledge representation. It then moves from traditional RAG into graph-based retrieval, showing how knowledge graphs can work alongside documents, embeddings, vector search, and LLMs.You will learn how to build knowledge graphs from unstructured information, extract entities and relationships with LLMs, resolve duplicate entities, design graph schemas, and build GraphRAG knowledge pipelines.The book then explores graph retrieval and reasoning, including graph traversal, semantic search, hybrid graph-and-vector retrieval, query expansion, multi-hop retrieval, entity-centric reasoning, and relationship-aware generation.Building on the agent foundations introduced in Book 1, the book moves into agentic GraphRAG, showing how AI agents can plan retrieval strategies, dynamically explore knowledge graphs, use retrieval tools, and perform multi-hop reasoning across connected information.You will also learn how knowledge graphs can become persistent agent memory, supporting long-term knowledge, contextual information, knowledge updates, and knowledge-aware agents.Finally, the book covers advanced GraphRAG architectures, including hierarchical, dynamic, temporal, community-based, and multi-agent GraphRAG, along with evaluation, security, governance, scalability, performance, and production deployment.Inside this book, you will learn how to: - Build and model knowledge graphs for AI applications- Extract entities and relationships from documents using LLMs- Understand the differences between traditional RAG and GraphRAG- Combine graph, vector, and semantic retrieval- Perform multi-hop retrieval and graph-based reasoning- Build GraphRAG pipelines for LLM applications- Design agentic GraphRAG architectures- Use knowledge graphs as persistent agent memory- Build hybrid, dynamic, temporal, and multi-agent GraphRAG systems- Evaluate retrieval quality, groundedness, and reasoning- Secure, monitor, scale, and deploy production GraphRAG applicationsFrom RAG and knowledge graphs to graph reasoning and agentic GraphRAG, this book provides a practical foundation for building AI systems that can retrieve, connect, understand, and reason over knowledge.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - AI is moving beyond systems that simply generate text. The next generation of intelligent applications can interpret goals, reason about tasks, use tools, maintain context, make decisions, and execute multi-step workflows.But building reliable AI agents requires more than connecting an LLM to a prompt. Building Agentic and Autonomous AI Agents provides a practical engineering guide to designing, developing, evaluating, securing, and deploying modern agentic AI systems. It takes you from the foundations of AI agents to autonomous workflows, tool use, memory, reasoning, planning, orchestration, and multi-agent architectures.You will learn how to: - Understand the architecture and core principles behind agentic AI- Design LLM-powered agents that can reason and make decisions- Build agents that use tools, APIs, databases, files, and external services- Implement short-term, long-term, and persistent agent memory- Design task decomposition, planning, reflection, and adaptive reasoning loops- Build sequential, conditional, iterative, event-driven, and human-in-the-loop workflows- Move from predefined workflows toward controlled autonomous execution- Design multi-agent systems with specialized roles, delegation, coordination, and supervision- Connect retrieval-augmented generation to AI agents- Test agent decisions, tool usage, reliability, and task completion- Protect agents against prompt injection, unauthorized actions, and unsafe tool use- Monitor, evaluate, optimize, deploy, and scale production-ready agentic applications- Understand advanced architectures such as ReAct-style, planning-based, reflection-based, memory-augmented, and multi-agent systemsRather than treating AI agents as mysterious autonomous systems, this book approaches them as engineered software systems with models, state, tools, memory, control loops, policies, and measurable outcomes.Whether you are a developer exploring LLM agents, an AI engineer building production systems, or a technical professional seeking to understand autonomous AI architectures, this book provides a structured path from LLM applications to intelligent, tool-using, reasoning, and autonomous systems.This is Book 1 of The Agentic AI and GraphRAG Engineering Series.It establishes the foundations you will need before moving into the knowledge-aware architectures explored in Book 2, Building Intelligent AI with GraphRAG.…

Taschenbuch. Condition: Neu. Neuware - No more prompts. Begin grounding. Large Language Models have changed the way we interact with machines, but they have a fatal flaw for enterprise applications: they rely on statistical probability, not deterministic truth. They hallucinate facts, struggle with multi-hop reasoning, and don't have the structured logic necessary for mission-critical systems. Standard vector-based Retrieval-Augmented Generation (RAG) attempts to remedy this, but viewing data as a flat ocean of semantic similarity is not enough. To build AI that actually knows, AI that is verifiable, explainable, and trustworthy, you must move from flat data to interconnected data. You want knowledge graph. This is your architectural blueprint for the next 10 years of artificial intelligence. This book will teach you how to build neuro-symbolic artificial intelligence systems that bridge the gap between deep learning and semantic web technologies, bringing the linguistic fluency of LLMs together with the rigorous, deterministic logic of Knowledge Graphs. Whether you're trying to remove hallucinations from your company's internal chatbot or designing autonomous AI agents that can handle complex enterprise data, this book provides the theoretical foundation and the production-ready code you need to succeed.- What You'll LearnGraph Data Foundations: Understand domain-driven design for scalable ontologies, and transform messy data into structured graph databases. Automated Knowledge Extraction Use LLMs, Named Entity Recognition (NER) and relationship extraction to automatically build Knowledge Graphs from unstructured text, PDFs and documents. GraphRAG: Go beyond traditional vector search. Design hybrid retrieval pipelines enabling true multi-hop reasoning using semantic embeddings and topological graph traversal. Machine Learning on Graphs: Leverage Graph Neural Networks (GNNs), node classification, and link prediction to discover hidden insights and structural patterns your LLM cannot infer itself. Create Autonomous AI Agents: Build end-to-end agents that leverage your Knowledge Graph as a map of the environment to drive memory, multi-step planning and autonomous decision-making. Production Deployment: Scale, secure, and govern knowledge-aware artificial intelligence systems in an enterprise setting.- Who This Book Is ForThis book is written for software engineers, data scientists, AI researchers, and technical architects ready to take their work beyond simple API wrappers. If you know Python, and understand databases and LLMs at a basic level, you have all you need to start building intelligent, graph-powered systems. The future of AI is not strictly neural. It's neuro-symbolic. Learn to build systems that don't just talk, but understand.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Artificial intelligence is transforming the way software is built, Tasks that once required hours of manual coding, debugging, and testing can now be accelerated through intelligent collaboration with AI. The developers who learn to integrate these tools into their workflow won't simply write code faster, they'll design better systems, solve more complex problems, and bring ideas to life with greater efficiency.Mastering AI Application Development with GPT-5 Codex is a practical guide for developers, software engineers, technical professionals, and ambitious learners who want to build modern AI-powered software from the ground up. Rather than treating AI as a simple code generator, this book teaches you how to use GPT-5 Codex as a collaborative development partner throughout the entire software lifecycle, from planning and architecture to implementation, testing, deployment, and ongoing improvement.Through clear explanations and hands-on examples, you'll discover how to create intelligent software that solves real-world problems while adopting development practices that improve productivity, maintainability, and code quality.Inside this book, you'll learn how to: Understand the capabilities and practical role of GPT-5 Codex in modern software development.Generate clean, modular, and maintainable code for real-world projects.Design software architectures with AI-assisted planning and technical reasoning.Build RESTful APIs and backend services using modern development frameworks.Create AI-powered web applications and full-stack software solutions.Develop intelligent AI agents capable of automating complex development tasks.Integrate databases, external APIs, authentication, and cloud services into AI-powered systems.Implement Retrieval-Augmented Generation (RAG) and vector search to build context-aware applications.Debug, refactor, optimize, and test software using AI-assisted workflows.Deploy scalable applications using containers, cloud platforms, and modern DevOps practices.Build production-ready AI solutions with security, reliability, and maintainability in mind.Transform ideas into real software products that can be expanded, deployed, and monetized.More than a collection of coding examples, this book emphasizes practical software engineering principles that remain valuable as AI technologies continue to evolve. You'll learn how to think like an AI-enabled developer, combining human creativity, engineering judgment, and intelligent automation to build software that is reliable, scalable, and ready for real-world use.Whether you're creating APIs, SaaS platforms, AI assistants, automation tools, or full-stack systems, the techniques in this book will help you streamline development, improve code quality, and accelerate delivery without sacrificing sound engineering practices.If you're ready to move beyond using AI as a coding assistant and start leveraging it as a true software development partner, Mastering AI Application Development with GPT-5 Codex will provide the knowledge, practical skills, and confidence to build the next generation of intelligent software. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Have you ever wanted to create your own video games but weren't sure where to begin?Godot 4 for Game Creators is a practical, beginner-friendly guide designed to help you build a solid foundation in modern game development using one of the world's most powerful open-source game engines. Whether your goal is to create 2D platformers, immersive 3D experiences, or interactive prototypes, this book walks you through the essential skills with clear explanations, hands-on projects, and real-world examples.You don't need previous programming or game development experience. Starting from the fundamentals, you'll learn how to navigate the Godot editor, organize game projects, write clean GDScript code, create engaging gameplay systems, and transform your ideas into fully playable games.Rather than overwhelming you with theory, this book focuses on learning by doing. Every chapter builds upon the previous one, allowing you to gain confidence as you create increasingly sophisticated game mechanics, environments, user interfaces, animations, audio systems, and interactive experiences.Inside this book, you'll learn how to: Install and configure Godot 4 for game developmentNavigate the editor and organize professional game projectsMaster scenes, nodes, and reusable game architectureWrite clean and efficient GDScript codeCreate interactive 2D and 3D game worldsDesign responsive player controls and camera systemsBuild enemies, collectibles, physics interactions, and gameplay mechanicsCreate intuitive menus and user interfacesAdd animations, sound effects, music, and visual polishSave player progress and manage game dataOptimize performance for smoother gameplayDebug and test your projects effectivelyExport and publish games for desktop, web, and mobile platformsApply game development best practices used by independent developersThroughout the book, you'll complete practical projects that reinforce each new concept while building the confidence to create original games of your own. By the end, you'll understand not only how Godot works, but also the design principles and development workflows that professional game creators use every day.Whether you're a student, hobbyist, aspiring indie developer, educator, or someone exploring a new creative career, this book provides the knowledge and practical experience needed to begin your game development journey with confidence.If you're ready to stop dreaming about making games and start building them, Godot 4 for Game Creators is the perfect place to begin. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Large language models can generate powerful responses, but they do not automatically have access to the knowledge your applications need. Traditional Retrieval-Augmented Generation (RAG) improves this by connecting LLMs to external information, yet many complex questions require more than finding similar text. They require understanding entities, relationships, context, and connected information.Building Intelligent AI with GraphRAG explores how knowledge graphs and GraphRAG can transform retrieval into a more connected and knowledge-aware process.Starting with the foundations of knowledge graphs, this book explains entities, relationships, properties, schemas, graph databases, and knowledge representation. It then moves from traditional RAG into graph-based retrieval, showing how knowledge graphs can work alongside documents, embeddings, vector search, and LLMs.You will learn how to build knowledge graphs from unstructured information, extract entities and relationships with LLMs, resolve duplicate entities, design graph schemas, and build GraphRAG knowledge pipelines.The book then explores graph retrieval and reasoning, including graph traversal, semantic search, hybrid graph-and-vector retrieval, query expansion, multi-hop retrieval, entity-centric reasoning, and relationship-aware generation.Building on the agent foundations introduced in Book 1, the book moves into agentic GraphRAG, showing how AI agents can plan retrieval strategies, dynamically explore knowledge graphs, use retrieval tools, and perform multi-hop reasoning across connected information.You will also learn how knowledge graphs can become persistent agent memory, supporting long-term knowledge, contextual information, knowledge updates, and knowledge-aware agents.Finally, the book covers advanced GraphRAG architectures, including hierarchical, dynamic, temporal, community-based, and multi-agent GraphRAG, along with evaluation, security, governance, scalability, performance, and production deployment.Inside this book, you will learn how to: Build and model knowledge graphs for AI applicationsExtract entities and relationships from documents using LLMsUnderstand the differences between traditional RAG and GraphRAGCombine graph, vector, and semantic retrievalPerform multi-hop retrieval and graph-based reasoningBuild GraphRAG pipelines for LLM applicationsDesign agentic GraphRAG architecturesUse knowledge graphs as persistent agent memoryBuild hybrid, dynamic, temporal, and multi-agent GraphRAG systemsEvaluate retrieval quality, groundedness, and reasoningSecure, monitor, scale, and deploy production GraphRAG applicationsFrom RAG and knowledge graphs to graph reasoning and agentic GraphRAG, this book provides a practical foundation for building AI systems that can retrieve, connect, understand, and reason over knowledge. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. AI is moving beyond systems that simply generate text. The next generation of intelligent applications can interpret goals, reason about tasks, use tools, maintain context, make decisions, and execute multi-step workflows.But building reliable AI agents requires more than connecting an LLM to a prompt. Building Agentic and Autonomous AI Agents provides a practical engineering guide to designing, developing, evaluating, securing, and deploying modern agentic AI systems. It takes you from the foundations of AI agents to autonomous workflows, tool use, memory, reasoning, planning, orchestration, and multi-agent architectures.You will learn how to: Understand the architecture and core principles behind agentic AIDesign LLM-powered agents that can reason and make decisionsBuild agents that use tools, APIs, databases, files, and external servicesImplement short-term, long-term, and persistent agent memoryDesign task decomposition, planning, reflection, and adaptive reasoning loopsBuild sequential, conditional, iterative, event-driven, and human-in-the-loop workflowsMove from predefined workflows toward controlled autonomous executionDesign multi-agent systems with specialized roles, delegation, coordination, and supervisionConnect retrieval-augmented generation to AI agentsTest agent decisions, tool usage, reliability, and task completionProtect agents against prompt injection, unauthorized actions, and unsafe tool useMonitor, evaluate, optimize, deploy, and scale production-ready agentic applicationsUnderstand advanced architectures such as ReAct-style, planning-based, reflection-based, memory-augmented, and multi-agent systemsRather than treating AI agents as mysterious autonomous systems, this book approaches them as engineered software systems with models, state, tools, memory, control loops, policies, and measurable outcomes.Whether you are a developer exploring LLM agents, an AI engineer building production systems, or a technical professional seeking to understand autonomous AI architectures, this book provides a structured path from LLM applications to intelligent, tool-using, reasoning, and autonomous systems.This is Book 1 of The Agentic AI and GraphRAG Engineering Series.It establishes the foundations you will need before moving into the knowledge-aware architectures explored in Book 2, Building Intelligent AI with GraphRAG. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. No more prompts. Begin grounding. Large Language Models have changed the way we interact with machines, but they have a fatal flaw for enterprise applications: they rely on statistical probability, not deterministic truth. They hallucinate facts, struggle with multi-hop reasoning, and don't have the structured logic necessary for mission-critical systems. Standard vector-based Retrieval-Augmented Generation (RAG) attempts to remedy this, but viewing data as a flat ocean of semantic similarity is not enough. To build AI that actually knows, AI that is verifiable, explainable, and trustworthy, you must move from flat data to interconnected data. You want knowledge graph. This is your architectural blueprint for the next 10 years of artificial intelligence. This book will teach you how to build neuro-symbolic artificial intelligence systems that bridge the gap between deep learning and semantic web technologies, bringing the linguistic fluency of LLMs together with the rigorous, deterministic logic of Knowledge Graphs. Whether you're trying to remove hallucinations from your company's internal chatbot or designing autonomous AI agents that can handle complex enterprise data, this book provides the theoretical foundation and the production-ready code you need to succeed.What You'll LearnGraph Data Foundations: Understand domain-driven design for scalable ontologies, and transform messy data into structured graph databases. Automated Knowledge Extraction Use LLMs, Named Entity Recognition (NER) and relationship extraction to automatically build Knowledge Graphs from unstructured text, PDFs and documents. GraphRAG: Go beyond traditional vector search. Design hybrid retrieval pipelines enabling true multi-hop reasoning using semantic embeddings and topological graph traversal. Machine Learning on Graphs: Leverage Graph Neural Networks (GNNs), node classification, and link prediction to discover hidden insights and structural patterns your LLM cannot infer itself. Create Autonomous AI Agents: Build end-to-end agents that leverage your Knowledge Graph as a map of the environment to drive memory, multi-step planning and autonomous decision-making. Production Deployment: Scale, secure, and govern knowledge-aware artificial intelligence systems in an enterprise setting.Who This Book Is ForThis book is written for software engineers, data scientists, AI researchers, and technical architects ready to take their work beyond simple API wrappers. If you know Python, and understand databases and LLMs at a basic level, you have all you need to start building intelligent, graph-powered systems. The future of AI is not strictly neural. It's neuro-symbolic. Learn to build systems that don't just talk, but understand. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…