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Mastering Large Language Models
Mastering Large Language Models
Knygos.lt klubas Knygos.lt nariams
70,48 €
-30%
Įprastai
100,69 €
  • Planuojame turėti už 41 d.
This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.Starting with the fundamentals-LLM architecture, tokenization, APIs, and fine-tuning-the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that c…

Mastering Large Language Models (el. knyga) (skaityta knyga) | knygos.lt

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Aprašymas

This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.

Starting with the fundamentals-LLM architecture, tokenization, APIs, and fine-tuning-the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance-helping readers move confidently from basic understanding to complex applications.

Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability-and innovation into impact.

What you will learn:

  • Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps.
  • Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.
  • Design AI agents capable of planning and executing complex workflows for automation and decision-making.
  • Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.

Who this book is for:

Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.

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This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.

Starting with the fundamentals-LLM architecture, tokenization, APIs, and fine-tuning-the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance-helping readers move confidently from basic understanding to complex applications.

Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability-and innovation into impact.

What you will learn:

  • Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps.
  • Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.
  • Design AI agents capable of planning and executing complex workflows for automation and decision-making.
  • Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.

Who this book is for:

Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.

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