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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Reviewing our use case – ML system design for NLP classification in a Jupyter Notebook

In this section, we will walk through a hands-on example. We will follow the steps we presented previously for articulating the problem, designing the solution, and evaluating the results. This section portrays the process that an ML developer goes through when working on a typical project in the industry. Refer to the notebook at https://colab.research.google.com/drive/1ZG4xN665le7X_HPcs52XSFbcd1OVaI9R?usp=sharing for more information.

The business objective

In this scenario, we are working for a financial news agency. Our objective is to publish news about companies and products in real time.

The technical objective

The CTO derives several technical objectives from the business objective. One objective is for the ML team: given a stream of financial tweets in real time, detect those tweets that discuss information about companies or products.

The pipeline

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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

Table of Contents (14) Chapters

Reviewing our use case – ML system design for NLP classification in a Jupyter Notebook

The business objective

The technical objective

The pipeline

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Authors (2)

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