This research paper proposes a unified framework for Riemannian deep learning, generalizing batch normalization and multinomial logistic regression to broad classes of Lie groups and gyrogroups. It also introduces novel neural networks for geometric representations, including hyperbolic space and full-rank correlation matrices. This work has potential implications for developing more robust and adaptive AI models.