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Applied Deep Learning on Graphs

You're reading from   Applied Deep Learning on Graphs Leverage graph data for business applications using specialized deep learning architectures

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781835885963
Length 250 pages
Edition 1st Edition
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Authors (2):
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Lakshya Khandelwal Lakshya Khandelwal
Author Profile Icon Lakshya Khandelwal
Lakshya Khandelwal
Subhajoy Das Subhajoy Das
Author Profile Icon Subhajoy Das
Subhajoy Das
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1: Foundations of Graph Learning
2. Chapter 1: Introduction to Graph Learning FREE CHAPTER 3. Chapter 2: Graph Learning in the Real World 4. Chapter 3: Graph Representation Learning 5. Part 2: Advanced Graph Learning Techniques
6. Chapter 4: Deep Learning Models for Graphs 7. Chapter 5: Graph Deep Learning Challenges 8. Chapter 6: Harnessing Large Language Models for Graph Learning 9. Part 3: Practical Applications and Implementation
10. Chapter 7: Graph Deep Learning in Practice 11. Chapter 8: Graph Deep Learning for Natural Language Processing 12. Chapter 9: Building Recommendation Systems Using Graph Deep Learning 13. Chapter 10: Graph Deep Learning for Computer Vision 14. Part 4: Future Directions
15. Chapter 11: Emerging Applications 16. Chapter 12: The Future of Graph Learning 17. Index 18. Other Books You May Enjoy

Part 3: Practical Applications and Implementation

In this part of the book, you will dive into the practical implementation of graph deep learning across various domains. You will learn how to apply graph-based approaches to natural language processing, build recommendation systems, and leverage graph structures in computer vision applications.

This part has the following chapters:

  • Chapter 7, Graph Deep Learning in Practice
  • Chapter 8, Graph Deep Learning for Natural Language Processing
  • Chapter 9, Building Recommendation Systems Using Graph Deep Learning
  • Chapter 10, Graph Deep Learning for Computer Vision
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