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

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

13. Other Books You May Enjoy

Technical requirements

For this chapter, the following will be necessary:

Programming knowledge: Familiarity with Python programming is a must, since the open source models, OpenAI’s API, and LangChain are all illustrated using Python code.
Access to OpenAI’s API: An API key from OpenAI will be required to explore closed source models. This can be obtained by creating an account with OpenAI and agreeing to their terms of service.
Open source models: Access to the specific open source models mentioned in this chapter will be necessary. These can be accessed and downloaded from their respective repositories or via package managers such as pip or conda.
A local development environment: A local development environment setup with Python installed is required. An Integrated Development Environment (IDE) such as PyCharm, Jupyter Notebook, or a simple text editor can be used. Note that we recommend a free Google Colab notebook, as it encapsulates all these...