Geometric Deep Learning approaches a broad class of ML problems from the perspectives of symmetry and invariance, providing a common blueprint for the “zoo” of neural network architectures. In the last post in our series on the origins of Geometric Deep Learning, we look at the precursors of Graph Neural Networks. — In the last post from the “Towards Geometric Deep Learning” series, we discuss how early prototypes of GNNs emerged in the field of chemistry in the 1960s. This post is based on the introduction chapter of the book M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković, Geometric Deep…