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Applied Deep Learning on Graphs
Applied Deep Learning on Graphs

Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures

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Profile Icon Lakshya Khandelwal Profile Icon Subhajoy Das
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Paperback Dec 2024 250 pages 1st Edition
eBook
₱579.99 ₱2040.99
Paperback
₱2551.99
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Arrow left icon
Profile Icon Lakshya Khandelwal Profile Icon Subhajoy Das
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Free Trial
Paperback Dec 2024 250 pages 1st Edition
eBook
₱579.99 ₱2040.99
Paperback
₱2551.99
Subscription
Free Trial
eBook
₱579.99 ₱2040.99
Paperback
₱2551.99
Subscription
Free Trial

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Table of content icon View table of contents Preview book icon Preview Book

Applied Deep Learning on Graphs

Part 1: Foundations of Graph Learning

In the first part of the book, you will get an overview of the fundamental concepts of graph learning, including basic definitions, real-world applications, and core representation techniques. You will learn about the essential building blocks needed to understand graph-based machine learning, practical use cases across industries, and various methods for representing graph data in machine learning contexts.

This part has the following chapters:

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Key benefits

  • Explore graph data in real-world systems and leverage graph learning for impactful business results
  • Dive into popular and specialized deep neural architectures like graph convolutional and attention networks
  • Learn how to build scalable and productionizable graph learning solutions
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs). This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision. By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies.

Who is this book for?

For data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.

What you will learn

  • Discover how to extract business value through a graph-centric approach
  • Develop a basic understanding of learning graph attributes using machine learning
  • Identify the limitations of traditional deep learning with graph data and explore specialized graph-based architectures
  • Understand industry applications of graph deep learning, including recommender systems and NLP
  • Identify and overcome challenges in production such as scalability and interpretability
  • Perform node classification and link prediction using PyTorch Geometric

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 27, 2024
Length: 250 pages
Edition : 1st
Language : English
ISBN-13 : 9781835885963
Category :

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Product Details

Publication date : Dec 27, 2024
Length: 250 pages
Edition : 1st
Language : English
ISBN-13 : 9781835885963
Category :

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Table of Contents

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