Specialized deep learning architectures exploit the intrinsic regularities arising from the underlying structure of the physical world. Geometric deep learning aims to expose these regularities through unified geometric principles. Doing this allows us to understand why some deep learning architectures are especially successful and gives a constructive procedure to incorporate prior physical knowledge into neural architectures.