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

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

13. Other Books You May Enjoy

Technical requirements

To successfully navigate through this chapter, certain technical prerequisites are necessary, as follows:

Programming knowledge: A strong understanding of Python is essential, as it’s the primary language used for most DL and NLP libraries.
Machine learning fundamentals: A good grasp of basic ML concepts such as training/testing data, overfitting, underfitting, accuracy, precision, recall, and F1 score will be valuable.
DL basics: Familiarity with DL concepts and architectures, including neural networks, backpropagation, activation functions, and loss functions, will be essential. Knowledge of RNNs and CNNs would be advantageous but not strictly necessary as we will focus more on transformer architectures.
NLP basics: Some understanding of basic NLP concepts such as tokenization, stemming, lemmatization, and word embeddings (such as Word2Vec or GloVe) would be beneficial.
Libraries and frameworks: Experience with libraries such...

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