Within deep learning, Euclidean geometry is the default basis for deep neural networks, yet the naive assumption that such a topology is optimal for all data types and tasks does not necessarily hold. A growing body of evidence suggests that data and the representations we aim to learn can be better captured through learning in corresponding geometries that exhibit non-Euclidean structures. Interest in non-Euclidean deep learning has grown dramatically in recent years, driven by advancing methodologies, libraries, and applications. The 2nd Beyond Euclidean workshop brings together computer vision researchers and keynote speakers who share an interest in exploring non-Euclidean geometry.