Vincze kalman (40 risultati)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Taschenbuch. Condizione: Neu. Neuware - Azure can feel overwhelming when you are just getting started. This book makes it manageable.Azure Without the Overwhelm is a practical, beginner-friendly introduction to cloud computing for readers who want to stop clicking through endless menus and start building something real.Instead of trying to teach every Azure service, this book focuses on the small set of skills you actually need to complete your first useful cloud project. Step by step, you will take a simple Python Flask application from your computer to the cloud, publish it with Azure App Service, work with Blob Storage, manage the resources behind your application, troubleshoot common problems, monitor costs, and clean up your environment responsibly.You do not need previous Azure experience, advanced cloud knowledge, or enterprise IT skills. The lessons are designed to help first-time users understand what they are doing, why each resource exists, and how the pieces fit together.Inside, you will learn how to: - Navigate the Azure Portal without getting lost in unnecessary services- Understand subscriptions, regions, resource groups, and cloud resources- Prepare and test a simple Flask web application locally- Deploy a real application to Azure App Service- Update and redeploy your live application- Create Azure Blob Storage and manage files in the cloud- Understand the relationship between your web app, App Service plan, and storage account- Use logs and built-in diagnostics to investigate simple application problems- Review project costs and create a basic Azure budget- Safely remove resources when your learning project is completeThe book uses one continuous project so every new concept has a clear purpose. You will not create random resources just to see what they do. Each step builds on the one before it, helping you develop a practical mental model of Azure while gaining hands-on experience.Whether you are a student exploring cloud computing, a developer moving beyond local applications, an IT professional building cloud skills, or a career switcher looking for a practical introduction to Azure, this book gives you a focused place to begin.You do not need to master Azure in a weekend. You need to understand enough to build, deploy, manage, and confidently continue learning.Start with one project. Learn the workflow. Build your cloud foundation.…

Taschenbuch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. Neuware - Move beyond your first successful AI API call and learn how to build AI features your application can actually depend on.Connecting software to an AI model is easier than ever. The harder part is turning that connection into a feature that handles real user input, produces useful results, survives failures, controls usage, and fits cleanly into the rest of your application.AI APIs for Real Applications is a practical, beginner-friendly guide for developers who want to understand how hosted AI models fit into real software.Instead of jumping between unrelated examples, you will build and improve one continuous project: an AI-powered Support Assistant. Starting with a simple API request, you will gradually develop a more complete and reliable integration while learning why each part of the workflow matters.You will learn how to: - Connect a JavaScript and Node.js application to a hosted AI model- Create and protect API credentials- Build reusable AI requests with clear instructions and user input- Work with API responses and structured JSON output- Parse and validate model-generated application data- Keep AI requests securely on the server side- Handle authentication, network, request, and service failures- Add timeouts, retries, backoff, and fallback behavior- Manage conversation context across multiple requests- Stream responses for a more responsive user experience- Understand tokens, rate limits, usage, cost, and performance- Test AI-powered features and detect regressions- Log requests and failures safely- Treat model output as untrusted application data- Prepare an AI integration for real usersThis book focuses on the skills that remain useful even as individual models, SDKs, and AI providers change. You will learn the underlying request-and-response workflow, how your application should control it, and how to make model output useful to ordinary software.You do not need experience training machine-learning models or managing AI infrastructure. Basic programming knowledge is enough to begin.If you can work with variables, functions, objects, and a development environment, this book will help you take the next step from experimenting with AI APIs to building practical AI-powered application features with greater confidence and reliability.…

Taschenbuch. Condizione: Neu. Neuware - Build intelligent AI agents, automate complex workflows, and create practical multi-agent applications with CrewAI-even if you are just getting started.Artificial intelligence is moving beyond simple chatbots. Modern AI agents can research information, use tools, make decisions, collaborate with other agents, and complete multi-step tasks with minimal supervision. However, building these systems can feel overwhelming when most tutorials assume advanced programming or AI experience.CrewAI Made Simple provides a clear, beginner-friendly path into agentic AI development. Through practical explanations, step-by-step examples, and realistic projects, you will learn how to use CrewAI and Python to design specialized agents, assign tasks, coordinate crews, connect external tools, and build reliable AI-powered workflows.Inside this book, you will learn how to: - Understand AI agents, multi-agent systems, and LLM-powered automation- Install Python, CrewAI, dependencies, and development tools correctly- Configure language models, API keys, and environment variables- Create agents with effective roles, goals, backstories, and responsibilities- Design tasks and pass information between workflow stages- Build and coordinate collaborative multi-agent crews- Connect search tools, APIs, databases, files, and external resources- Manage agent memory, context, reasoning, and decision-making- Develop practical research, content creation, and business-analysis systems- Debug common errors and test agent workflows effectively- Improve reliability, accuracy, performance, and output quality- Prepare CrewAI applications for deployment, maintenance, and scalingYou will also build practical projects such as an AI research assistant, an automated content creation workflow, a business analysis crew, and systems for handling repetitive knowledge work. The book progresses from foundational concepts to production-focused practices, including security, cost management, monitoring, testing, and optimization.Whether you are a beginner exploring AI development, a Python programmer interested in agentic AI, a business professional seeking smarter automation, or a developer building intelligent applications, this guide will help you understand not only how CrewAI works, but also how to use it effectively in real-world projects.Stop relying on isolated prompts. Start building organized teams of AI agents that can research, reason, collaborate, and complete meaningful work.…

Taschenbuch. Condizione: Neu. Neuware - Learn JAX from the ground up and discover how its most powerful features fit together in a practical machine learning workflow.JAX brings together NumPy-style array computing, automatic differentiation, compilation, vectorization, and hardware acceleration. These capabilities make it an exciting tool for modern machine learning, but they can also make JAX feel difficult to approach when you are just getting started.JAX Fundamentals for Modern Machine Learning provides a clear, practical introduction designed for Python users who want to understand JAX without being overwhelmed by research-level examples or unnecessary complexity.Instead of treating JAX as a collection of isolated functions, this book teaches the fundamentals progressively. You will begin with arrays and JAX-friendly Python, then build your understanding of gradients, JIT compilation, vectorization, neural networks, and accelerated execution as each concept becomes useful.Throughout the book, you will gradually build a handwritten digit classifier with a neural network, giving every major JAX concept a practical purpose.Inside this book, you will learn how to: - Create, reshape, inspect, and manipulate JAX arrays- Understand shapes, data types, broadcasting, and immutable updates- Write pure functions and manage state in a JAX-friendly way- Work correctly with JAX random number keys- Organize model parameters using PyTrees- Calculate derivatives and gradients with jax.grad- Compute values and gradients together with jax.value_and_grad- Speed up numerical functions using jax.jit- Understand tracing, recompilation, and common JIT errors- Vectorize computations and batches with jax.vmap- Combine vmap, grad, and jit in practical workflows- Build a neural network from basic JAX operations- Create loss and accuracy functions- Compute gradients and update model parameters during training- Build and run a compiled training step- Evaluate a trained classifier and make predictions- Save and reload learned model parameters- Understand CPU, GPU, and TPU execution in JAX- Measure performance and work with asynchronous execution- Diagnose common JAX errors involving shapes, tracers, devices, randomness, and trainingThe examples stay focused on the skills you actually need to understand JAX. You will not be asked to memorize a large framework or copy a finished machine learning system without understanding how it works.By the end of the book, you will have built a complete beginner-friendly machine learning project while developing a practical understanding of the JAX tools that make modern numerical computing fast, composable, and powerful.Whether you are coming from Python, NumPy, machine learning, or another numerical computing library, this book will give you the foundation you need to start using JAX with confidence. …

Paperback. Condizione: New.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Build AI that answers from the information you trust, not just from what a language model already knows.Large language models are powerful, but they do not automatically know your private documents, internal knowledge, product information, policies, research, or other specialized sources. Retrieval-Augmented Generation (RAG) solves that problem by connecting language models with relevant external knowledge before an answer is generated.Building Useful AI with RAG is a practical, beginner-friendly guide to understanding that workflow by building it yourself.Instead of overwhelming you with multiple frameworks, databases, and advanced techniques at once, this book focuses on the core RAG process and develops one continuous project: a Trusted Knowledge Assistant that can search a collection of documents, retrieve relevant information, and use that evidence to produce grounded answers.You will move step by step from raw documents to a complete working RAG application while understanding what each component contributes and how the pieces fit together.Inside the book, you will learn how to: Understand why language models need external knowledge and where RAG fits into modern AI applicationsPrepare and normalize trusted documents for reliable retrievalDivide documents into useful chunks without losing important contextUnderstand and create embeddings for documents and user questionsBuild a searchable vector indexUse semantic similarity to retrieve the most relevant knowledgePreserve metadata so retrieved information remains connected to its original sourceBuild reusable retrieval logic for an AI assistantCombine retrieved knowledge with a language model to create an end-to-end RAG pipelineGenerate answers that stay grounded in the available evidenceDisplay sources and handle questions the knowledge base cannot answerEvaluate retrieval quality, relevance, groundedness, and answer completenessDiagnose weak RAG results and improve chunking, queries, metadata filtering, and retrieval settingsUnderstand when hybrid search and reranking can improve resultsBuild a simple question-and-answer interface for the completed assistantUpdate and reindex the knowledge base as your documents changeThis book emphasizes understanding over copying code. You will build simple versions first, inspect how they behave, identify their limitations, and improve them deliberately. The goal is to help you understand the reasoning behind RAG well enough to adapt the workflow to your own projects instead of depending on a single framework or tool.Whether you want to build an AI assistant for company documents, product manuals, policies, technical documentation, research material, course content, or another controlled knowledge source, the same foundational workflow applies.By the end of the book, you will understand how information moves from trusted documents to retrieval, context, and finally a grounded AI answer, and you will have built the complete workflow yourself.If you are comfortable with basic Python and want to move from experimenting with language models to building useful AI applications that can answer from real knowledge, this book gives you a clear place to start. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: new. Paperback. Build intelligent AI agents, automate complex workflows, and create practical multi-agent applications with CrewAI-even if you are just getting started.Artificial intelligence is moving beyond simple chatbots. Modern AI agents can research information, use tools, make decisions, collaborate with other agents, and complete multi-step tasks with minimal supervision. However, building these systems can feel overwhelming when most tutorials assume advanced programming or AI experience.CrewAI Made Simple provides a clear, beginner-friendly path into agentic AI development. Through practical explanations, step-by-step examples, and realistic projects, you will learn how to use CrewAI and Python to design specialized agents, assign tasks, coordinate crews, connect external tools, and build reliable AI-powered workflows.Inside this book, you will learn how to: - Understand AI agents, multi-agent systems, and LLM-powered automation- Install Python, CrewAI, dependencies, and development tools correctly- Configure language models, API keys, and environment variables- Create agents with effective roles, goals, backstories, and responsibilities- Design tasks and pass information between workflow stages- Build and coordinate collaborative multi-agent crews- Connect search tools, APIs, databases, files, and external resources- Manage agent memory, context, reasoning, and decision-making- Develop practical research, content creation, and business-analysis systems- Debug common errors and test agent workflows effectively- Improve reliability, accuracy, performance, and output quality- Prepare CrewAI applications for deployment, maintenance, and scalingYou will also build practical projects such as an AI research assistant, an automated content creation workflow, a business analysis crew, and systems for handling repetitive knowledge work. The book progresses from foundational concepts to production-focused practices, including security, cost management, monitoring, testing, and optimization.Whether you are a beginner exploring AI development, a Python programmer interested in agentic AI, a business professional seeking smarter automation, or a developer building intelligent applications, this guide will help you understand not only how CrewAI works, but also how to use it effectively in real-world projects.Stop relying on isolated prompts. Start building organized teams of AI agents that can research, reason, collaborate, and complete meaningful work. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.