[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 , then an overview explaining several important methods  and a separate post on spectral convolution .
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.
 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
 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
 Spectral Graph Convolution Explained and Implemented Step By Step: https://towardsdatascience.com/spectral-graph-convolution-explained-and-implemented-step-by-step-2e495b57f801