Mastering Large Language Models with Python

Unleash the Power of Advanced Natural Language Processing for Enterprise Innovation and Efficiency Using Large Language Models (LLMs) with Python

Raj R

• Publisher: Orange Education Pvt Ltd
• Year: 2024
• ISBN:
  9788197081804
• Topic: Computer Science
• Subtopic: Natural Language Processing
• Index: Computer Science

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 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

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication Page
• About the Author
• About the Technical Reviewers
• Acknowledgements
• Preface
• Errata
• 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