By encoding an understanding of the translational symmetry of the physical world, convolutional neural networks (CNNs) have revolutionised computer vision.

We recently published a technical blog post discussing how the principles underlying the success of CNNs may be transferred to the range of problems for which the data exhibits complex geometry, such as the sphere.

An example of spherical data.

To learn more please see the full article published on Towards Data Science.