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

Why subscribe?

13. Other Books You May Enjoy

To get the most out of this book

All the code presented in this book is in the form of a Jupyter notebook. All the code was developed with Python 3.10.X and is expected to work on later versions as well.

Software/hardware covered in the book	Operating system requirements
Access to a Python environment via one of the following: Accessing Google Colab, which is free and easy from any browser on any device (recommended) A local/cloud development environment of Python with the ability to install public packages and access OpenAI’s API	Windows, macOS, or Linux
Sufficient computation resources, as follows: The previously recommended free access to Google Colab includes a free GPU instance If opting to avoid Google Colab, the local/cloud environment should have a GPU for several code examples

Software/hardware covered in the book

Operating system requirements

Access to a Python environment via one of the following:

Accessing Google Colab, which is free and easy from any browser on any device (recommended)
A local/cloud development environment of Python with the ability to install public packages and access OpenAI’s API

Windows, macOS, or Linux

Sufficient computation resources, as follows:

The previously recommended free access to Google Colab includes a free GPU instance
If opting to avoid Google Colab, the local/cloud environment should have a GPU for several code examples

As the code examples in this book have a diversified set of use cases, for some of the advanced LLM solutions, you will need an OpenAI account, which will allow an API key.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.