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Mastering NLP from Foundations to LLMs

You're reading from   Mastering NLP from Foundations to LLMs Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Length 340 pages
Edition 1st Edition
Languages
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Authors (2):
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Meysam Ghaffari Meysam Ghaffari
Author Profile Icon Meysam Ghaffari
Meysam Ghaffari
Lior Gazit Lior Gazit
Author Profile Icon Lior Gazit
Lior Gazit
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction 2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP FREE CHAPTER 3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing 4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance 5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques 6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models 7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation 8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG 9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs 10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI 11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts 12. Index 13. Other Books You May Enjoy

Summary

In this concluding chapter of our exploration into the dynamic world of NLP and LLMs, we have had the privilege of engaging with experts across various fields. Their insightful discussions have illuminated intricate developments, legal considerations, operational approaches, regulatory influences, and emerging capabilities of LLMs. Through their expert lenses, we delved into pressing issues such as creating equitable datasets, advancing NLP technologies, navigating privacy protections in research, restructuring organizations around AI, and anticipating breakthroughs in learning paradigms.

The dialogue with these luminaries has underscored a common theme: the intersection of technological innovation with ethical, legal, and organizational considerations. As we ponder strategies to mitigate biases in datasets, envision the future of hybrid learning paradigms, and assess the impact of foundation models on data ownership, it becomes clear that the evolution of NLP and LLMs is...

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