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Paperback. Condición: new. Paperback. Hermes Agent in Action: Build Autonomous AI Applications with Memory, MCP, Tool Calling, RAG, and Multi-Agent Workflows Artificial intelligence is rapidly moving beyond chatbots and simple text generation. Today's AI applications can remember previous interactions, retrieve trusted knowledge, communicate with external systems, use tools, collaborate with other agents, and automate complex business processes. Hermes Agent in Action is a practical, hands-on guide that teaches you how to design, build, deploy, and scale these next-generation autonomous AI applications using the Hermes Agent framework.Whether you are a software developer, AI engineer, machine learning practitioner, automation specialist, or technology professional, this book provides the practical knowledge needed to build intelligent applications that solve real business problems.Starting with the fundamentals, you'll learn how autonomous AI applications differ from traditional AI systems and how Hermes Agent coordinates reasoning, memory, tool execution, and external integrations. From there, you'll build increasingly capable applications by implementing persistent memory, managing conversations, designing effective agent instructions, integrating APIs, connecting databases, working with files, and extending your applications through the Model Context Protocol (MCP).You'll also master Retrieval-Augmented Generation (RAG), learning how to build intelligent knowledge-aware applications using vector databases, semantic search, and document retrieval techniques that produce more accurate and reliable responses. As your skills grow, you'll explore multi-agent architectures that enable specialized AI agents to collaborate on complex workflows and solve problems more efficiently.Rather than stopping with individual concepts, this book walks you through building complete production-oriented applications, including an intelligent research assistant, an AI customer support application, an AI coding assistant, and workflow automation solutions. These projects demonstrate how memory, MCP, tool calling, RAG, and multi-agent collaboration work together to create practical AI systems that organizations can deploy with confidence.Building an AI application is only part of the process. This book also covers the engineering practices required for production environments. You'll learn how to test and evaluate AI systems, implement authentication and authorization, secure sensitive data, apply responsible AI principles, deploy applications with Docker, monitor production workloads, optimize performance and operational costs, design enterprise-grade architectures, and scale autonomous AI applications to support real users. Inside This Book, You'll Learn How To: Build autonomous AI applications with Hermes Agent from the ground up.Design intelligent agents with memory and long-running conversations.Implement tool calling for APIs, databases, files, and external services.Integrate Model Context Protocol (MCP) into production applications.Build Retrieval-Augmented Generation (RAG) systems using vector databases.Design and orchestrate collaborative multi-agent workflows.Develop practical AI applications for research, coding, customer support, and automation.Secure, test, deploy, monitor, optimize, and scale AI applications using proven engineering practices.Build enterprise-ready AI systems that are reliable, maintainable, and production-ready.By the time you finish this book, you won't just understand Hermes Agent-you'll know how to engineer intelligent AI applications that integrate with modern software systems, automate meaningful work, and operate reliably in production. Start building autonomous AI applications today and turn your ideas into production- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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

Condición: New.

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

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

Condición: As New. Unread book in perfect condition.

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

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

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

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

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

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

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. Hermes Agent in Action: Build Autonomous AI Applications with Memory, MCP, Tool Calling, RAG, and Multi-Agent Workflows Artificial intelligence is rapidly moving beyond chatbots and simple text generation. Today's AI applications can remember previous interactions, retrieve trusted knowledge, communicate with external systems, use tools, collaborate with other agents, and automate complex business processes. Hermes Agent in Action is a practical, hands-on guide that teaches you how to design, build, deploy, and scale these next-generation autonomous AI applications using the Hermes Agent framework.Whether you are a software developer, AI engineer, machine learning practitioner, automation specialist, or technology professional, this book provides the practical knowledge needed to build intelligent applications that solve real business problems.Starting with the fundamentals, you'll learn how autonomous AI applications differ from traditional AI systems and how Hermes Agent coordinates reasoning, memory, tool execution, and external integrations. From there, you'll build increasingly capable applications by implementing persistent memory, managing conversations, designing effective agent instructions, integrating APIs, connecting databases, working with files, and extending your applications through the Model Context Protocol (MCP).You'll also master Retrieval-Augmented Generation (RAG), learning how to build intelligent knowledge-aware applications using vector databases, semantic search, and document retrieval techniques that produce more accurate and reliable responses. As your skills grow, you'll explore multi-agent architectures that enable specialized AI agents to collaborate on complex workflows and solve problems more efficiently.Rather than stopping with individual concepts, this book walks you through building complete production-oriented applications, including an intelligent research assistant, an AI customer support application, an AI coding assistant, and workflow automation solutions. These projects demonstrate how memory, MCP, tool calling, RAG, and multi-agent collaboration work together to create practical AI systems that organizations can deploy with confidence.Building an AI application is only part of the process. This book also covers the engineering practices required for production environments. You'll learn how to test and evaluate AI systems, implement authentication and authorization, secure sensitive data, apply responsible AI principles, deploy applications with Docker, monitor production workloads, optimize performance and operational costs, design enterprise-grade architectures, and scale autonomous AI applications to support real users. Inside This Book, You'll Learn How To: Build autonomous AI applications with Hermes Agent from the ground up.Design intelligent agents with memory and long-running conversations.Implement tool calling for APIs, databases, files, and external services.Integrate Model Context Protocol (MCP) into production applications.Build Retrieval-Augmented Generation (RAG) systems using vector databases.Design and orchestrate collaborative multi-agent workflows.Develop practical AI applications for research, coding, customer support, and automation.Secure, test, deploy, monitor, optimize, and scale AI applications using proven engineering practices.Build enterprise-ready AI systems that are reliable, maintainable, and production-ready.By the time you finish this book, you won't just understand Hermes Agent-you'll know how to engineer intelligent AI applications that integrate with modern software systems, automate meaningful work, and operate reliably in production. Start building autonomous AI applications today and turn your ideas int Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware - Julia for Machine Learning and AI: Build Intelligent Models, Train Neural Networks, and Develop High-Performance AI Applications If you want to move beyond theory and start building real machine learning systems that are fast, reliable, and scalable, this book gives you a clear path forward.Julia for Machine Learning and AI is designed to help you understand not just how models work, but how to build them from the ground up using Julia. You will learn how to work with data, design models, train neural networks, and optimize performance using tools like Flux.jl, all while keeping your code efficient and production-ready.The book starts by helping you build a solid foundation in Julia for numerical computing and data handling. From there, you move into core machine learning concepts, including regression, classification, and model evaluation. You will not just learn definitions-you will implement each concept with working code and understand how it behaves in practice.As you progress, you will explore how to build and train neural networks, understand how optimization works, and learn how to improve model accuracy and stability. You will also see how to handle larger datasets, optimize performance, and scale your systems using parallel and efficient computation techniques.What makes this book especially practical is its focus on complete systems. You will learn how to design data pipelines, deploy models, integrate them into real applications, and monitor them over time. By the end, you will be able to build end-to-end AI systems that go beyond experiments and deliver real value.Throughout the book, you will work on hands-on projects that reflect real-world challenges. These projects help you connect concepts to practical use, so you are not just learning-you are building.Whether you are a developer looking to enter machine learning, a data professional aiming to improve performance and scalability, or someone who wants a clear and structured way to learn AI with Julia, this book gives you the tools and understanding you need.>Start now, write the code, train the models, and create systems that solve real problems.…

Taschenbuch. Condición: Neu. Neuware.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. The Ollama Handbook: Build Powerful Local AI Applications with Open-Source LLMs, RAG, Agents, and Automation Artificial intelligence is no longer limited to cloud platforms and expensive APIs. With Ollama, you can run powerful open-source large language models directly on your own computer, giving you greater privacy, lower costs, and complete control over your AI applications. Whether you're a developer, software engineer, data professional, student, or AI enthusiast, The Ollama Handbook provides the practical knowledge you need to build modern local AI solutions with confidence.Starting with the fundamentals, this book guides you through installing and configuring Ollama, understanding how large language models work, selecting the right open-source models, and mastering prompt engineering. From there, you'll learn how to develop AI-powered applications using the Ollama CLI, REST API, and Python while integrating advanced technologies such as Retrieval-Augmented Generation (RAG), vector databases, AI agents, LangChain, LlamaIndex, and Open WebUI.Moving beyond theory, you'll build intelligent chatbots, private knowledge assistants, document processing systems, workflow automation tools, and production-ready AI applications. You'll also discover practical techniques for optimizing performance, managing memory and context, securing local AI deployments, troubleshooting common issues, and integrating multiple AI components into complete end-to-end platforms. Inside this book, you'll learn how to: Install, configure, and manage Ollama effectively.Run and customize open-source LLMs using Modelfiles.Build applications with the Ollama CLI, REST API, and Python.Implement Retrieval-Augmented Generation (RAG) using embeddings and vector databases.Develop autonomous AI agents with tool calling and workflow automation.Integrate Ollama with LangChain, LlamaIndex, and Open WebUI.Optimize performance, resource usage, and production deployments.Apply responsible AI, privacy, and security best practices.Build real-world AI projects that you can extend for personal or professional use.Throughout the book, you'll find practical examples, complete code samples, reusable prompt templates, command references, troubleshooting guidance, and proven development practices that help you move from experimentation to building reliable AI applications.If you're ready to unlock the full potential of local AI and build intelligent, private, and production-ready applications without relying on cloud-based services, The Ollama Handbook is the practical guide you've been looking for. Get your copy today and start building the next generation of local AI applications with Ollama. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Agentic Spec-Driven Software Engineering: A Practical Guide to Requirements Planning, System Design, and AI-Assisted Development Agentic Spec-Driven Software Engineering introduces a modern approach to building software in the age of AI. As development teams increasingly rely on intelligent assistants, code generators, and autonomous agents, the ability to create clear, structured, and actionable specifications has become one of the most valuable skills in software engineering.This book shows you how to move beyond traditional code-first development and adopt a specification-first mindset that places requirements, architecture, quality, and business objectives at the center of every engineering decision. You will learn how AI agents can assist with requirements discovery, system design, architecture evaluation, testing, deployment planning, governance, and continuous improvement while remaining aligned with well-defined specifications.Through practical examples, proven frameworks, real-world case studies, and reusable templates, you will discover how to transform ideas into structured requirements, create high-quality specifications, design scalable architectures, generate implementation plans, automate validation workflows, and build effective multi-agent engineering systems.Inside this book, you will learn how to: - Create clear, complete, and testable software specifications- Use AI agents for requirements planning and discovery- Design architectures directly from documented requirements- Build APIs, data models, and integration strategies from specifications- Generate tests and quality assurance workflows automatically- Maintain alignment between specifications, code, and operations- Establish governance, traceability, and compliance processes- Build AI-native engineering teams and workflows- Prepare for the future of autonomous software developmentWhether you are a software engineer, architect, technical lead, product manager, consultant, founder, or technology leader, this book provides a practical framework for designing and delivering software systems that are more predictable, maintainable, scalable, and aligned with business goals.The future of software engineering belongs to teams that can effectively collaborate with AI while maintaining clarity, control, and engineering discipline. Start building that future today and learn how specification-driven thinking can transform the way you design, develop, and deliver software. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Claude Code and AI Agents: Design Autonomous Systems, Build Multi-Agent Workflows, and Create Scalable AI Applications with Modern LLM Tools This book gives you a complete, practical path to building intelligent software powered by modern language models. It moves beyond simple prompts and shows you how to design systems that can plan, reason, act, and adapt in real environments.You will learn how to work with Claude-powered workflows and agent-based architectures from the ground up. The journey begins with a clear understanding of how AI agents operate, including how prompts, context, tools, and memory come together to produce reliable behavior. From there, you move into designing systems that can handle real tasks-retrieving information, interacting with APIs, coordinating multiple agents, and managing complex workflows.The book takes a hands-on approach throughout. You will build working applications step by step, including autonomous research assistants, multi-agent customer support systems, and intelligent workflow automation tools. Each example is designed to reflect real production scenarios so you can apply what you learn immediately.You will also gain a strong foundation in backend and frontend integration. This includes designing APIs for agent-driven systems, managing sessions and state, handling streaming responses, and creating user interfaces that feel responsive and trustworthy. Along the way, you will learn how to optimize performance, control costs, and scale your systems to handle real demand.Security, reliability, and responsible AI are treated as essential parts of the process. You will understand how to apply guardrails, validate inputs and outputs, monitor system behavior, and build applications that users can rely on.By the end of this book, you will not just understand how AI systems work-you will know how to design, build, and deploy them with confidence.If you are ready to move from experimenting with AI to building complete, production-ready systems that solve real problems, this book will guide you every step of the way. Start building intelligent systems that think, act, and scale-your next breakthrough application begins here. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Julia for Machine Learning and AI: Build Intelligent Models, Train Neural Networks, and Develop High-Performance AI Applications If you want to move beyond theory and start building real machine learning systems that are fast, reliable, and scalable, this book gives you a clear path forward.Julia for Machine Learning and AI is designed to help you understand not just how models work, but how to build them from the ground up using Julia. You will learn how to work with data, design models, train neural networks, and optimize performance using tools like Flux.jl, all while keeping your code efficient and production-ready.The book starts by helping you build a solid foundation in Julia for numerical computing and data handling. From there, you move into core machine learning concepts, including regression, classification, and model evaluation. You will not just learn definitions-you will implement each concept with working code and understand how it behaves in practice.As you progress, you will explore how to build and train neural networks, understand how optimization works, and learn how to improve model accuracy and stability. You will also see how to handle larger datasets, optimize performance, and scale your systems using parallel and efficient computation techniques.What makes this book especially practical is its focus on complete systems. You will learn how to design data pipelines, deploy models, integrate them into real applications, and monitor them over time. By the end, you will be able to build end-to-end AI systems that go beyond experiments and deliver real value.Throughout the book, you will work on hands-on projects that reflect real-world challenges. These projects help you connect concepts to practical use, so you are not just learning-you are building.Whether you are a developer looking to enter machine learning, a data professional aiming to improve performance and scalability, or someone who wants a clear and structured way to learn AI with Julia, this book gives you the tools and understanding you need.You are not here to read about machine learning. You are here to build it. Start now, write the code, train the models, and create systems that solve real problems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…