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Taschenbuch. Zustand: Neu. Neuware - LLMs are changing how people write, learn, code, search, and build software, but they are often explained with too much hype or too much jargon.LLMs Explained is a practical, beginner-friendly guide to understanding large language models and using them wisely.This book explains what LLMs are, how they fit into the broader AI landscape, what they do well, where they struggle, and how to work with them in real-world tasks and applications. Instead of focusing on heavy math or vendor-specific tricks, it gives you a clear map of the ideas behind modern AI tools: prompts, tokens, context windows, hallucinations, structured outputs, retrieval, embeddings, tool use, MCP, skills, agents, privacy, safety, and evaluation.Inside, you will learn how to: - understand what an LLM is and how it generates responses- see how LLMs relate to AI, generative AI, and modern assistant products- write clearer prompts and instructions- recognize where LLMs are useful and where they are risky- understand tokens, context, memory, and model limits- reduce hallucinations and verify important answers- use structured outputs for more reliable AI workflows- understand embeddings, semantic search, retrieval, and RAG- see how tools, function calling, MCP, skills, and agents fit together- think about privacy, security, cost, and responsible useWhether you are a developer, product manager, student, writer, creator, or simply curious, this book gives you a practical foundation for understanding LLMs without getting lost in buzzwords. LLMs Explained is a clear starting point for anyone who wants to understand modern AI tools and use them with more confidence, clarity, and care.…

Taschenbuch. Zustand: Neu. Neuware - Build more reliable AI coding workflows without chasing the perfect prompt.AI coding agents can write code, inspect repositories, run tools, and complete multi-step tasks. But useful output is not the same as reliable engineering.Harness Engineering is a practical guide to building the surrounding workflow that helps agents work more safely and predictably. Using a small companion project, agent_harness_lab, the book shows how vague tasks, weak context, loose permissions, and unclear verification can be improved step by step.You will learn how to: - Make repositories easier for agents to navigate- Turn vague requests into executable task briefs- Choose useful context without overwhelming the agent- Set safer tool and permission boundaries- Use plans, checkpoints, tests, and evidence more effectively- Apply human review where it adds the most value- Improve the harness over time by learning from repeated failuresThis is not a prompt-writing guide or a vendor-specific manual. It is a practical framework for software engineers, technical leads, and builders who want AI agents to become part of a more trustworthy engineering workflow.The tools will keep changing. The discipline behind a strong harness will last.…

Taschenbuch. Zustand: Neu. Neuware - Part of the Practical Tech Guide Series, this book focuses on what actually matters: how to take a folder of images and turn it into a working model you understand and control. You don't need a machine learning background. If you can run a command and open a browser, you can follow this guide from start to finish. Inside, you'll learn: - What image classification is and how it works (in simple terms)- How to prepare and organise your own image dataset- How to train models locally on your own machine- How to choose the right architecture (PyTorch and TensorFlow)- How to read accuracy, loss, and training results with confidence- How to improve your model with better data and tuning>Everything is built around a simple, open-source training tool with a browser-based interface - so you can focus on results, not boilerplate code. No deep math. No unnecessary theory. No black boxes. Whether you're experimenting with AI, building a small project, or working with sensitive data you don't want to upload to the cloud, this guide gives you a clear, practical path. If you want to train AI image models locally - and actually understand what you're doing - this book is for you.…