Description: Geometric deep learning is a field that extends deep learning methods to non-Euclidean domains such as graphs and manifolds. Unlike traditional approaches that operate in Euclidean spaces, geometric deep learning focuses on the intrinsic structure of data, allowing models to learn more effective representations in complex contexts. This approach is based on graph theory and differential geometry, enabling the manipulation and analysis of data that cannot be adequately represented in a flat space. Convolutional neural networks, for example, have been adapted to work with graph-shaped data, leading to significant advances in various fields such as computer vision and natural language processing. Furthermore, geometric deep learning is fundamental for edge AI, where devices must process data in real-time without relying on central servers. This field also intersects with neuromorphic computing, which seeks to emulate the functioning of the human brain to improve efficiency in processing complex data. In summary, geometric deep learning represents a crucial evolution in artificial intelligence, allowing models to learn more effectively in unstructured and complex environments.
