Geometric Deep Learning (GDL) extends traditional deep learning to non-Euclidean data like graphs, manifolds, and meshes. Unlike classical neural networks that operate on grid-like data (e.g., images/videos), GDL leverages geometric structures to model complex relationships in data. This field is pivotal for tasks such as social network analysis, molecular property prediction, and 3D object recognition.

🔍 Key Concepts

  • Graph Neural Networks (GNNs): Learn representations on graph-structured data
    Graph Neural Networks
  • Manifold Learning: Embed high-dimensional data into geometric spaces
    Manifold Learning
  • Spherical CNNs: Process data on curved surfaces (e.g., globe maps)
    Spherical CNNs

📚 Applications

  • Chemistry: Predict molecular properties using graph representations
  • Physics: Model particle interactions in non-Euclidean spaces
  • Computer Vision: Analyze 3D shapes and point clouds

🌐 Further Reading

For an in-depth exploration of GDL frameworks and tools, check out our guide on Graph-Based Machine Learning Techniques. This resource covers PyTorch Geometric, TensorFlow Graph Nets, and benchmark datasets like QM9 and ZINC.

💡 Tip: Use geometric deep learning to unlock hidden patterns in your structured data!