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

View More author details

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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Key technical trends around LLMs and AI

In this section, we cover what we identify as key trends in the field of NLP and LLMs.

We will start with the technical trends, and later, we will touch on the softer cultural trends.

Computation power – the engine behind LLMs

As technology has advanced, especially in computing, many areas in tech have thrived, particularly NLP and LLMs. It’s not just about faster calculations and bigger parameter space; it’s about new possibilities and reshaping our digital world. In this section, we’ll explore how this growth in computing has been foundational for NLP and LLMs today, focusing on their purpose, worth, and influence.

Purpose – paving the way for progress

In the initial days of AI and ML, the models were rudimentary—not due to a lack of imagination or intent, but because of restrictive computational boundaries. Tasks that we now consider basic, such as simple pattern recognitions, were...