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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Text classification using TF-IDF

One-hot encoded vector is a good approach to perform classification. However, one of its weaknesses is that it does not consider the importance of different words based on different documents. To solve this issue, using TF-IDF can be helpful.

TF-IDF is a numerical statistic that is used to measure the importance of a word in a document within a document collection. It helps reflect the relevance of words in a document, considering not only their frequency within the document but also their rarity across the entire document collection. The TF-IDF value of a word increases proportionally to its frequency in a document but is offset by the frequency of the word in the entire document collection.

Here’s a detailed explanation of the mathematical equations involved in calculating TF-IDF:

Term frequency (TF): The TF of a word, t, in a document, d, represents the number of times the word occurs in the document, normalized by the total...