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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Questions and answers

What is natural language processing (NLP)?
- Q: What defines NLP in the field of artificial intelligence?
- A: NLP is a subfield of AI focused on enabling computers to understand, interpret, and generate human language in a way that is both natural and meaningful to human users.
Initial strategies in machine processing of natural language.
- Q: What is the importance of preprocessing in NLP?
- A: Preprocessing, including tasks such as removing stop words and applying stemming or lemmatization, is crucial for cleaning and preparing text data, thereby improving the performance of machine learning algorithms on NLP tasks.
The synergy of NLP and machine learning (ML).
- Q: How does machine learning contribute to advancements in NLP?
- A: ML, especially techniques such as statistical language modeling and deep learning, drives NLP forward by enabling algorithms to learn from data, predict word sequences, and perform tasks such as language understanding and sentiment analysis more effectively.
Introduction to math and statistics in NLP
- Q: Why are mathematical foundations important in NLP?
- A: Mathematical foundations such as linear algebra, statistics, and probability are essential for understanding and developing the algorithms that underpin NLP techniques, from basic preprocessing to complex model training.
Advancements in NLP – the role of pre-trained language models
- Q: How have pre-trained models such as BERT and GPT influenced NLP?
- A: Pre-trained models, trained on vast amounts of text data, can be fine-tuned for specific tasks such as sentiment analysis or language translation, significantly simplifying the development of NLP applications and enhancing task performance.
Understanding transformers in language models
- Q: Why are transformers considered a breakthrough in NLP?
- A: Transformers process words in parallel and use attention mechanisms to understand word context within sentences, significantly improving a model’s ability to handle the complexities of human language.