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[P] Multipart Tutorial on Graph Neural Networks for Computer Vision and Beyond with PyTorch examples

I published a multipart “Tutorial on Graph Neural Networks for Computer Vision and Beyond” starting from some basics [1], then an overview explaining several important methods [2] and a separate post on spectral convolution [3].

I know there are a lot of blog posts on graph networks already, but in my tutorial I tried to explain key (and sometimes complicated) ideas in very simple terms from a computer vision perspective, so it should be good for those with a computer vision and machine learning background. I provide detailed Python and PyTorch examples to clarify differences between methods.

I wasn’t sure if to publish it here due to this discussion (Regarding beginner’s guides: https://www.reddit.com/r/MachineLearning/comments/co37ut/regarding_beginners_guides/ ), but hopefully it will be appreciated here. Otherwise, feel free to downvote or remove.

Any questions or feedback is very welcome, especially, if you notice some mistakes or confusing info.

[1] Part 1 of the Tutorial: convolution on graphs and differences between simple fully-connected neural networks (MLPs) and graph networks: https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d

[2] Anisotropic, Dynamic, Spectral and Multiscale Filters Defined on Graphs: https://towardsdatascience.com/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-2-be6d71d70f49

[3] Spectral Graph Convolution Explained and Implemented Step By Step: https://towardsdatascience.com/spectral-graph-convolution-explained-and-implemented-step-by-step-2e495b57f801

submitted by /u/bknyazev
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.