Applied Deep Learning on Graphs

eBook Details:

• Paperback: 250 pages
• Publisher: WOW! eBook (December 27, 2024)
• Language: English
• ISBN-10: 1835885977
• ISBN-13: 978-1835885963

eBook Description:

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

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 Applied Deep Learning on Graphs 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.

• 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

By the end of this Applied Deep Learning on Graphs 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.