Published on December 12, 2018 by

Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I’ll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid’s laws help define certain types of data, so I’ll cover some geometry background as well. Enjoy!

Code for this video:
https://github.com/llSourcell/pytorch_geometric

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More learning resources:
http://sungsoo.github.io/2018/02/01/g…
http://geometricdeeplearning.com/
https://arxiv.org/abs/1611.08097
http://3ddl.stanford.edu/CVPR17_Tutor…
https://github.com/rusty1s/pytorch_ge…
https://pemami4911.github.io/paper-su…

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