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

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Free Download Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures by Lakshya Khandelwal, Subhajoy Das
English | December 27, 2024 | ISBN: 1835885977 | 250 pages | EPUB | 7.94 Mb
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 FeaturesExplore graph data in real-world systems and leverage graph learning for impactful business resultsDive into popular and specialized deep neural architectures like graph convolutional and attention networksLearn how to build scalable and productionizable graph learning solutionsBook 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 learnDiscover how to extract business value through a graph-centric approachDevelop a basic understanding of learning graph attributes using machine learningIdentify the limitations of traditional deep learning with graph data and explore specialized graph-based architecturesUnderstand industry applications of graph deep learning, including recommender systems and NLPIdentify and overcome challenges in production such as scalability and interpretabilityPerform node classification and link prediction using PyTorch GeometricWho 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 ContentsIntroduction to Graph LearningGraph Learning in the Real WorldGraph Representation LearningDeep Learning Models for GraphsGraph Deep Learning ChallengesHarnessing Large Language Models for Graph LearningGraph Deep Learning in PracticeGraph Deep Learning for Natural Language ProcessingBuilding Recommendation Systems Using Graph Deep LearningGraph Deep Learning for Computer VisionEmerging ApplicationsThe Future of Graph Learning

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