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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Language models

A language model is a statistical model in NLP that is designed to learn and understand the structure of human language. More specifically, it is a probabilistic model that is trained to estimate the likelihood of words when provided with a given word scenario. For instance, a language model could be trained to predict the next word in a sentence, given the previous words.

Language models are fundamental to many NLP tasks. They are used in machine translation, speech recognition, part-of-speech tagging, and named entity recognition, among other things. More recently, they have been used to create conversational AI models such as chatbots and personal assistants and to generate human-like text.

Traditional language models were often based on explicitly statistical methods, such as n-gram models, which consider only the previous n words when predicting the next word, or hidden Markov models (HMMs).

More recently, neural networks have become popular for creating...