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

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781804619186

Length 340 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (2):

Meysam Ghaffari

Lior Gazit

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Table of Contents (14) Chapters

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

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Summary

In this chapter, we embarked on a comprehensive journey through the key trends shaping the world of AI, with a particular emphasis on LLMs. At the very heart of these models lies computational power, which acts as the driving engine, enabling breakthroughs and amplifying their potential. With advancements in computational capabilities, we’re not only progressing faster but also unlocking new efficiencies that redefine the realm of possibilities.

Complementing this computational prowess are vast datasets, casting an indelible mark on NLP and LLMs. We have covered their significance in this chapter and learned that they serve pivotal roles. As we look ahead, the future of data availability in NLP promises to be a dynamic landscape, constantly evolving in response to these challenges.

LLMs themselves have undergone significant evolution; each iteration aimed at achieving greater scale and capability. We reviewed the impact these models possess and learned that they...