Geometric Deep Learning approaches a broad class of ML problems from the perspectives of symmetry and invariance, providing a common blueprint for neural network architectures as diverse as CNNs, GNNs, and Transformers. In a new series of posts, we study how these ideas have taken us from ancient Greece to convolutional neural networks. — In the third post from the “Towards Geometric Deep Learning series,” we discuss the first “geometric” neural networks: the Neocognitron and CNNs. This post is based on the introduction chapter of the book M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković, Geometric Deep Learning (to appear with MIT…