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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

Potential breakthroughs and challenges ahead

As the field of AI and, more specifically, LLMs continues to evolve, we are likely to witness several breakthroughs along with significant challenges. Here’s a look at what the future may hold:

  • Potential breakthroughs:
    • Advanced cognitive understanding: LLMs may develop a deeper understanding of context, sarcasm, and nuanced language, effectively managing tasks that require a high level of cognitive abilities
    • Multimodal capabilities: The ability to process and generate multimodal content, integrating text with images, audio, and video, could revolutionize how we interact with AI
    • Personalized AI interactions: Breakthroughs in personalization could lead to LLMs that adapt to individual user’s preferences, learning styles, and needs in real time
    • Generalized AI: We could move toward more generalized AI that can perform a variety of tasks without extensive retraining or fine-tuning
    • Quantum computing integration: Integration with...
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