Graph Neural Networks for Real-World AI Systems (Paperback)
Language: English
Published by Independently Published, 2025
- Softcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
£ 16.30
Quantity: 1 available
Add to basketItem description from seller
Paperback. Unlock the transformative power of Graph Neural Networks (GNNs) and elevate your AI systems with contextual intelligence and relational reasoning. This comprehensive guide bridges the gap between cutting-edge research and practical application, empowering data scientists, AI engineers, and machine learning practitioners to design, implement, and scale graph-based AI systems for real-world challenges.In today's complex data environments, relationships between entities are just as crucial as the entities themselves. Traditional deep learning approaches often struggle to capture these intricate connections, limiting performance in domains like recommendation systems, fraud detection, knowledge graphs, and social network analysis. This book dives deep into Graph Neural Networks-offering a hands-on roadmap to leverage GCN, GAT, GraphSAGE, and PyTorch Geometric for building high-performance, explainable, and scalable AI systems.What you'll gain from this book: Foundations of Graph Intelligence: Develop a solid understanding of graph theory, graph data structures, and relational representations as the backbone of modern AI systems.Architectures for Real-World Applications: Explore Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, with practical insights on selecting and customizing architectures for specific use cases.Scalable Training Pipelines: Learn to design efficient data pipelines, mini-batch training strategies, and distributed computing approaches for large-scale graphs.Integration with PyTorch Geometric: Master hands-on implementation, from preprocessing graph data to deploying GNN models using the widely adopted PyTorch Geometric framework.Case Studies & Practical Examples: Dive into real-world projects demonstrating social network analytics, knowledge graph completion, recommendation engines, and fraud detection using GNNs.Whether you are building AI systems for enterprise-scale applications or exploring the forefront of research in graph intelligence, this book equips you with the practical skills, architectural know-how, and strategic insights to harness the full potential of Graph Neural Networks. Ground your AI in relational reasoning, deliver explainable insights, and transform complex data into actionable intelligence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
Seller Inventory # 9798277250228
- Title
- Graph Neural Networks for Real-World AI Systems (Paperback)
- Author
- Tyrell Owen
- Publisher
- Independently Published
- Publication year
- 2025
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 13
- 9798277250228
In today’s complex data environments, relationships between entities are just as crucial as the entities themselves. Traditional deep learning approaches often struggle to capture these intricate connections, limiting performance in domains like recommendation systems, fraud detection, knowledge graphs, and social network analysis. This book dives deep into Graph Neural Networks—offering a hands-on roadmap to leverage GCN, GAT, GraphSAGE, and PyTorch Geometric for building high-performance, explainable, and scalable AI systems.
What you’ll gain from this book:
- Foundations of Graph Intelligence: Develop a solid understanding of graph theory, graph data structures, and relational representations as the backbone of modern AI systems.
- Architectures for Real-World Applications: Explore Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, with practical insights on selecting and customizing architectures for specific use cases.
- Scalable Training Pipelines: Learn to design efficient data pipelines, mini-batch training strategies, and distributed computing approaches for large-scale graphs.
- Integration with PyTorch Geometric: Master hands-on implementation, from preprocessing graph data to deploying GNN models using the widely adopted PyTorch Geometric framework.
- Case Studies & Practical Examples: Dive into real-world projects demonstrating social network analytics, knowledge graph completion, recommendation engines, and fraud detection using GNNs.
"Synopsis" may belong to another edition of this title.
Grand Eagle Retail
Bensenville, IL, U.S.A.
AbeBooks seller since October 12, 2005
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