Mastering Large Language Models with Python
Language: English
Published by Orange Education Pvt Ltd, 2024
Series: Book 1 of 3 - Generative AI Engineer — Python LLM Path
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 60.21
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - A Comprehensive Guide to Leverage Generative AI in the Modern EnterpriseBook 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 Contents1. The Basics of Large Language Models and Their Applications2. Demystifying Open-Source Large Language Models3. Closed-Source Large Language Models4. LLM APIs for Various Large Language Model Tasks5. Integrating Cohere API in Google Sheets6. Dynamic Movie Recommendation Engine Using LLMs7. Document-and Web-based QA Bots with Large Language Models8. LLM Quantization Techniques and Implementation9. Fine-tuning and Evaluation of LLMs10. Recipes for Fine-Tuning and Evaluating LLMs11. LLMOps - Operationalizing LLMs at Scale12. Implementing LLMOps in Practice Using MLflow on Databricks13. Mastering the Art of Prompt Engineering14. Prompt Engineering Essentials and Design Patterns15. Ethical Considerations and Regulatory Frameworks for LLMs16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index.…
Seller Inventory # 9788197081828
- Title
- Mastering Large Language Models with Python
- Author
- Raj Arun R
- Publisher
- Orange Education Pvt Ltd
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 8197081824
- ISBN 13
- 9788197081828
- Item weight
- 1,021 grams
- Dimensions
- 235x191x30 mm
- Series
- Book 1 of 3: Generative AI Engineer — Python LLM Path
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
"Synopsis" may belong to another edition of this title.
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