Build Llm Applications Python by Guha Prabir (13 results)

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: New.

Paperback. Condition: New.

paperback. Condition: Very Good.

Condition: New.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New.

Paperback. Condition: New.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Build LLM Applications Locally with Python, Ollama, LangChain, and Gradio: A Hands-On GuideBy Prabir GuhaUnlock the power of Large Language Models (LLMs) through practical, real-world application!This hands-on guide demystifies how LLMs work, how to run them locally with Ollama, and how to build cutting-edge applications with Python, LangChain, and Gradio - no cloud dependency required.Starting with the evolution of Natural Language Processing (NLP) from early rule-based systems to today's transformer-based LLMs like GPT and BERT, the book provides a solid technical foundation. You'll learn how to install and configure the Ollama framework to run models like LLaMA 3.1 on your own workstation, ensuring privacy, low latency, and no API costs.Through step-by-step examples, you'll build your first Python LLM applications, master prompting techniques, and explore LangChain - a powerful framework for chaining prompts, tools, and memory. Practical use cases include text summarization, generation, QA systems, and structured data extraction. The book also introduces Agentic Technology, allowing your LLM applications to reason dynamically and use external tools autonomously.You'll build user-friendly chat interfaces with Gradio, mimicking popular conversational AIs like ChatGPT, and dive into Retrieval-Augmented Generation (RAG) systems that enrich LLMs with domain-specific knowledge, such as querying documents like a Medicare Guide.Finally, the book discusses the major challenges facing LLMs - bias, hallucination, environmental impact - and explores future trends such as multimodal AI, model optimization, and autonomous AI agents.Whether you're a developer, researcher, or enthusiast, this guide equips you with the skills and tools to build intelligent, efficient, and domain-adaptive LLM applications - all locally and hands-on.Key Topics Covered: How LLMs work (Transformer models, Encoders, Decoders)Setting up the Ollama framework for local LLM executionBuilding LLM applications with PythonCrafting effective prompts for optimal model behaviorDeveloping advanced LLM apps with LangChainIntegrating agents for autonomous reasoningCreating conversational UIs using GradioImplementing Retrieval-Augmented Generation (RAG) systemsFuture challenges and trends in LLM evolutionIf you want to build and deploy your own LLM-powered systems locally - without relying on expensive cloud services - this book is your practical, hands-on guide. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…