Graph Neural Networks in Practice: Design, Train, and Apply GNNs for Real-World AI Systems - Tapa blanda

Sinopsis

Are you ready to solve problems that traditional AI can't? Discover the power of Graph Neural Networks.

This essential reading is for machine learning practitioners, data engineers, and students aiming to master Graph AI and its diverse applications. Equip yourself with the skills to build intelligent systems that understand relationships, predict complex behaviors, and generate novel insights from interconnected data.

Key features include:

• End-to-End Project Workflow: From graph modeling to GNN deployment and monitoring.

• Key Architectures Explained: Deep dive into GCN, GraphSAGE, GAT, and heterogeneous GNNs.

• Scalability Solutions: Tackle massive graphs with practical sampling and distributed training techniques.

• Real-World Impact: Explore mini case studies across cybersecurity, social network analysis, and smart cities.

• Ethical Deployment: Best practices for fairness, robustness, and privacy in GNNs.

• Hands-on with PyTorch Geometric and DGL.

"Graph Neural Networks in Practice" provides the practical knowledge to build impactful AI Systems on graph data.

"Sinopsis" puede pertenecer a otra edición de este libro.