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)
在現代企業中利用生成式人工智慧的綜合指南
書籍描述
《用 Python 精通大型語言模型》是一本不可或缺的資源，提供對大型語言模型（LLMs）的全面探索，提供有效利用這些變革性人工智慧模型所需的基本知識。從揭示 LLM 架構的複雜性到實際應用，如代碼生成和 AI 驅動的推薦系統，讀者將獲得在各種項目中實施 LLM 的寶貴見解。
本書涵蓋開源和專有的 LLM，深入探討基礎概念和進階技術，使專業人士能夠充分發揮這些模型的潛力。對於高效部署的量化技術、LLMOps 的運營策略以及倫理考量的詳細討論，確保了對 LLM 實施的全面理解。
通過真實案例研究、代碼片段和實用示例，讀者將自信地駕馭 LLM 的複雜性，為創新解決方案和組織成長鋪平道路。無論您是希望加深理解、推動有影響力的應用，還是領導 AI 驅動的倡議，本書都為您提供在動態人工智慧領域中脫穎而出的工具和見解。
目錄
1. 大型語言模型及其應用的基本知識
2. 解密開源大型語言模型
3. 專有大型語言模型
4. 各種大型語言模型任務的 LLM API
5. 在 Google Sheets 中整合 Cohere API
6. 使用 LLM 的動態電影推薦引擎
7. 基於文檔和網頁的大型語言模型問答機器人
8. LLM 量化技術及其實施
9. LLM 的微調與評估
10. LLM 微調與評估的食譜
11. LLMOps - 大規模運營 LLM
12. 在 Databricks 上使用 MLflow 實踐 LLMOps
13. 精通提示工程的藝術
14. 提示工程的基本要素和設計模式
15. LLM 的倫理考量和監管框架
16. 朝向可信的生成式人工智慧（受符號推理啟發的新框架）
索引
