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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
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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

Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI

Natural language processing (NLP) and large language models (LLMs) stand at the intersection of linguistics and artificial intelligence, serving as milestones in our understanding of human-computer interactions. Their story begins with basic rule-based systems, which, while innovative for their time, often stumbled due to the complex nuances and immensity of human language. The limitations of these systems highlighted the need for a shift, paving the way for the machine learning (ML) era, where data and pattern recognition prescribe the design and the models.

In this chapter, we will review key trends that have been emerging in NLP and LLMs, some of which are broad enough to capture the direction of AI as a whole. We will discuss those trends from a qualitative perspective as we aim to highlight their purpose, value, and impact. In the next sections, we’ll share our thoughts on what the future...

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