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Decoding Large Language Models

You're reading from   Decoding Large Language Models An exhaustive guide to understanding, implementing, and optimizing LLMs for NLP applications

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835084656
Length 396 pages
Edition 1st Edition
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Author (1):
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Irena Cronin Irena Cronin
Author Profile Icon Irena Cronin
Irena Cronin
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Table of Contents (22) Chapters Close

Preface 1. Part 1: The Foundations of Large Language Models (LLMs)
2. Chapter 1: LLM Architecture FREE CHAPTER 3. Chapter 2: How LLMs Make Decisions 4. Part 2: Mastering LLM Development
5. Chapter 3: The Mechanics of Training LLMs 6. Chapter 4: Advanced Training Strategies 7. Chapter 5: Fine-Tuning LLMs for Specific Applications 8. Chapter 6: Testing and Evaluating LLMs 9. Part 3: Deployment and Enhancing LLM Performance
10. Chapter 7: Deploying LLMs in Production 11. Chapter 8: Strategies for Integrating LLMs 12. Chapter 9: Optimization Techniques for Performance 13. Chapter 10: Advanced Optimization and Efficiency 14. Part 4: Issues, Practical Insights, and Preparing for the Future
15. Chapter 11: LLM Vulnerabilities, Biases, and Legal Implications 16. Chapter 12: Case Studies – Business Applications and ROI 17. Chapter 13: The Ecosystem of LLM Tools and Frameworks 18. Chapter 14: Preparing for GPT-5 and Beyond 19. Chapter 15: Conclusion and Looking Forward 20. Index 21. Other Books You May Enjoy

Preface

In Decoding Large Language Models, you will embark on a comprehensive journey, starting with the historical evolution of Natural Language Processing (NLP) and the development of Large Language Models (LLMs). The book explores the complex architecture of these models, making intricate concepts such as transformers and attention mechanisms accessible. As the journey progresses, it transitions into the practicalities of training and fine-tuning LLMs, providing hands-on guidance for real-world applications. The narrative then explores advanced optimization techniques and addresses the crucial aspect of ethical considerations in AI. In its final stages, the book offers a forward-looking perspective, preparing you for future developments such as GPT-5. This journey not only educates but also empowers you to skillfully implement and deploy LLMs in various domains.

By the end of this book, you will have gained a thorough understanding of the historical evolution and current state of LLMs in NLP. You will be proficient in the complex architecture of these models, including transformers and attention mechanisms. Your skills will extend to effectively training and fine-tuning LLMs for a variety of real-world applications. You will also have a strong grasp of advanced optimization techniques to enhance model performance. You will be well-versed in the ethical considerations surrounding AI, enabling you to deploy LLMs responsibly. Lastly, you will be prepared for emerging trends and future advancements in the field, such as GPT-5, equipping you to stay at the forefront of AI technology and its applications.

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