Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures (Paperback or Softback)

Khandelwal, Lakshya

ISBN 10: 1835885969 / ISBN 13: 9781835885963

Editore: Packt Publishing 12/27/2024, 2024

Riguardo questo articolo

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

Codice articolo BBS-9781835885963

Dati bibliografici

• Titolo: Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures (Paperback or Softback)
• Editore: Packt Publishing 12/27/2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 1835885969
• ISBN 13: 9781835885963
• Rilegatura: Paperback or Softback
• Condizione: New
• Peso: 0,96 libbre
• Tipo di libro: Book

Descrizione articolo

Riassunto

Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domains

Key Features

• 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

Book 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.

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

Who this book is 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.

Table of Contents

1. Introduction to Graph Learning
2. Graph Learning in the Real World
3. Graph Representation Learning
4. Deep Learning Models for Graphs
5. Graph Deep Learning Challenges
6. Harnessing Large Language Models for Graph Learning
7. Graph Deep Learning in Practice
8. Graph Deep Learning for Natural Language Processing
9. Building Recommendation Systems Using Graph Deep Learning
10. Graph Deep Learning for Computer Vision
11. Emerging Applications
12. The Future of Graph Learning

Informazioni sull'autore

Lakshya Khandelwal holds a bachelor's and master's degree from IIT Kanpur in mathematics and computer science and has 8+ years of experience in building scalable machine learning products for multiple tech giants. He has worked as a lead ML engineer with Samsung, building natural language intelligence for the very first version of Bixby. He has also worked as a data scientist with Adobe, developing search bid optimization solutions as part of the advertising cloud suite for major enterprises across the globe. In addition, he has led natural language and forecasting initiatives at Walmart, building next-generation AI products for millions of customers. Lakshya currently leads AI for AirMDR, building agentic AI for the cybersecurity domain.

Subhajoy Das is a staff data scientist with 7 years of experience under his belt. He graduated from IIT Kharagpur with a bachelor's and master's degree in mathematics and computing. Since then, he has worked in organizations at varying stages of growth: from fast-growing e-commerce start-ups such as Meesho to behemoths such as Adobe. He has driven several pivotal features in every company he has worked in, including building an end-to-end recommendation system for the Meesho app and curating interesting advertising using reinforcement learning-based optimizations in Adobe Advertising. He is currently working at Arista Networks, building AI-driven apps that are responsible for the cybersecurity of several Fortune 500 companies.