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Video recording of the course "Geometric Deep Learning" taught in the African Master in Machine Intelligence in July 2022 by Michael Bronstein (Oxford), Joan Bruna (NYU), Taco Cohen (Qualcomm), and Petar Veličković (DeepMind) Lecture 1: Symmetry through the centuries • First neural networks and the “Perceptron affair” • The curse of dimensionality • First geometric architectures: neocognitron and CNNs • Chemical precursors of GNNs • Geometric deep learning blueprint • The "5G" of Geometric deep learning • Course outline Slides: Lecture 7: Grids and Translations • Translation group • Shift operator • Linear invariants and equivariants • Fourier transform • Convolution • Fourier invariants • Deformation stability • Multiscale representations • Wavelets • Scattering • CNNs Slides: https://www.dropbox.com/s/xjwb6weegz9vuwz/AIMS%202022%20-%20Lecture%207%20-%20Grids.pdf?dl=0 Blog post: https://towardsdatascience.com/deriving-convolution-from-first-principles-4ff124888028?sk=0d77e2fd7863d457aeb2dac620dd133c Additional materials: www.geometricdeeplearning.com
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