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Applied Deep Learning on Graphs

You're reading from   Applied Deep Learning on Graphs Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781835885963
Length
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 FREE CHAPTER
2. Chapter 1: Introduction to Graph Learning 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

Do we need graphs?

The recent artificial intelligence (AI) revolution is the tip of the iceberg of a megatrend that has been impacting the computing industry for decades now. Over time, computing performance has increased exponentially against power consumed and cost; information storage costs have also decreased exponentially. To put this into perspective, while a terabyte of data can be stored in a disk costing around 100 US dollars in 2024, it would have taken more than a million dollars in the early 1990s!

Using computers and their derivative products, such as software, web applications, games, and multimedia content, has become deeply tied to our normal lifestyle. This dependence led to the need for understanding the behavior of all the interacting entities: humans, computer hardware, software such as web applications, and even organizations as a whole. The end goal was to find ways to make interactions more efficient, which could lead to unprecedented business opportunities...

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