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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Navigating the NLP Landscape: A Comprehensive Introduction

This book is aimed at helping professionals apply natural language processing (NLP) techniques to their work, whether they are working on NLP projects or using NLP in other areas, such as data science. The purpose of the book is to introduce you to the field of NLP and its underlying techniques, including machine learning (ML) and deep learning (DL). Throughout the book, we highlight the importance of mathematical foundations, such as linear algebra, statistics and probability, and optimization theory, which are necessary to understand the algorithms used in NLP. The content is accompanied by code examples in Python to allow you to pre-practice, experiment, and generate some of the development presented in the book.

The book discusses the challenges faced in NLP, such as understanding the context and meaning of words, the relationships between them, and the need for labeled data. The book also mentions the recent advancements in NLP, including pre-trained language models, such as BERT and GPT, and the availability of large amounts of text data, which has led to improved performance on NLP tasks.

The book will engage you by discussing the impact of language models on the field of NLP, including improved accuracy and effectiveness in NLP tasks, the development of more advanced NLP systems, and accessibility to a broader range of people.

We will be covering the following headings in the chapter: