r/MachineLearning - [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. Otherwise, feel free to downvote or remove. Any questions or feedback is very welcome, especially, if you notice some mistakes or confusing info.