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Mastering NLP from Foundations to LLMs

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

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Length 340 pages
Edition 1st Edition
Languages
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Authors (2):
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Meysam Ghaffari Meysam Ghaffari
Author Profile Icon Meysam Ghaffari
Meysam Ghaffari
Lior Gazit Lior Gazit
Author Profile Icon Lior Gazit
Lior Gazit
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Table of Contents (14) Chapters Close

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

Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG

In this dynamic era of Artificial Intelligence (AI) and Machine Learning (ML), understanding the vast assortment of available resources and learning how to utilize them effectively is vital. Large Language Models (LLMs) such as GPT-4 have revolutionized the field of Natural Language Processing (NLP) by showcasing unprecedented performance in diverse tasks, from content generation to complex problem-solving. Their immense potential extends not only to understanding and generating human-like text but also to bridging the gap between machines and humans, in terms of communication and task automation. Embracing the practical applications of LLMs can empower businesses, researchers, and developers to create more intuitive, intelligent, and efficient systems that cater to a wide range of requirements. This chapter offers a guide to setting up access to LLMs, walking you through using them and building pipelines...

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