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商品描述
A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise
Book Description
"Mastering Large Language Models with Python" is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects.
Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation.
Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence.
Table of Contents
1. The Basics of Large Language Models and Their Applications
2. Demystifying Open-Source Large Language Models
3. Closed-Source Large Language Models
4. LLM APIs for Various Large Language Model Tasks
5. Integrating Cohere API in Google Sheets
6. Dynamic Movie Recommendation Engine Using LLMs
7. Document-and Web-based QA Bots with Large Language Models
8. LLM Quantization Techniques and Implementation
9. Fine-tuning and Evaluation of LLMs
10. Recipes for Fine-Tuning and Evaluating LLMs
11. LLMOps - Operationalizing LLMs at Scale
12. Implementing LLMOps in Practice Using MLflow on Databricks
13. Mastering the Art of Prompt Engineering
14. Prompt Engineering Essentials and Design Patterns
15. Ethical Considerations and Regulatory Frameworks for LLMs
16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning)
Index
商品描述(中文翻譯)
《現代企業中利用生成式人工智慧的全面指南》
書籍描述：
《使用Python掌握大型語言模型》是一本不可或缺的資源，提供了對大型語言模型（LLM）進行全面探索的知識，讓讀者能夠有效地利用這些具有轉型能力的人工智慧模型。從揭示LLM架構的複雜性到實際應用，如代碼生成和基於人工智慧的推薦系統，讀者將獲得寶貴的洞察力，了解在各種項目中實施LLM的重要知識。
本書涵蓋了開源和專有的LLM，深入探討了基礎概念和高級技術，使專業人士能夠充分發揮這些模型的潛力。詳細討論了用於高效部署的量化技術、LLMOps的操作策略以及倫理考慮，確保對LLM實施有全面的理解。
通過實際案例研究、代碼片段和實際示例，讀者將自信地應對LLM的複雜性，為創新解決方案和組織增長鋪平道路。無論您是想加深理解、推動有影響力的應用還是領導人工智慧驅動的項目，本書都將為您提供在人工智慧的動態領域中取得成功所需的工具和見解。
目錄：
1. 大型語言模型及其應用的基礎知識
2. 解密開源大型語言模型
3. 專有大型語言模型
4. 用於各種大型語言模型任務的LLM API
5. 在Google Sheets中集成Cohere API
6. 使用LLM的動態電影推薦引擎
7. 基於LLM的文檔和網絡問答機器人
8. LLM量化技術和實施
9. LLM的微調和評估
10. LLM微調和評估的技巧
11. LLMOps-大規模運營LLM
12. 在Databricks上使用MLflow實踐LLMOps
13. 掌握提示工程的藝術
14. 提示工程的基本要素和設計模式
15. LLM的倫理考慮和監管框架
16. 走向可信的生成式人工智慧（受符號推理啟發的新框架）
索引
