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

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A winning synergy – the coming together of NLP and ML

ML is a subfield of AI that involves training algorithms to learn from data, allowing them to make predictions or decisions without those being explicitly programmed. ML is driving advancements in so many different fields, such as computer vision, voice recognition, and, of course, NLP.

Diving a little more into the specific techniques of ML, a particular technique used in NLP is statistical language modeling, which involves training algorithms on large text corpora to predict the likelihood of a given sequence of words. This is used in a wide range of applications, such as speech recognition, machine translation, and text generation.

Another essential technique is DL, which is a subfield of ML that involves training artificial neural networks on large amounts of data. DL models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been shown to be adequate for NLP tasks such as language understanding, text summarization, and sentiment analysis.

Figure 1.2 portrays the relationship between AI, ML, DL, and NLP: