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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What are LLMs and how are they different from LMs?

An LM is a type of ML model that is trained to predict the next word (or character or subword, depending on the granularity of the model) in a sequence, given the words that came before it (or in some models, the surrounding words). It’s a probabilistic model that is capable of generating text that follows a certain linguistic style or pattern.

Before the advent of Transformer-based models such as generative pretrained Transformers (GPTs) and Bidirectional Encoder Representations from Transformers (BERT), there were several other types of LMs widely used in NLP tasks. The following subsections discuss a few of them.

n-gram models

These are some of the simplest LMs. An n-gram model uses the (n-1) previous words to predict the nth word in a sentence. For example, in a bigram (2-gram) model, we would use the previous word to predict the next word. These models are easy to implement and computationally efficient, but they...