[Day 90] Probability: Univariate Models and colab 0 from XCS224W: ML with Graphs

 Hello :)
Today is Day 90!


A quick summary of today:


Probabilistic Machine Learning: An Introduction

Chapter 2: Probability: Univariate Models


Today I got added to the slack channel for XCS224W: ML with Graphs by Stanford, and I went over setting up the environment and over the 0th colab - some basics about graphs. I am not allowed to share any of the code from the colabs, but I found this similar tutorial that is public and I can talk over how I improved upon it. (I need to go over the animation part of this tutorial and showing the nodes in 3d space)

To avoid any risk of a lawyer sending me an email, I will just say how I improved upon the above tutorial. 

After training a GNN on KarateClub data from pytorch_geometric, the result looked like

And there was a conclusion under it saying how even a simple model, can learn to separate and classify the nodes. Given that final visualization, I was not sure how one can come up with that conclusion, so I looked around to learn a bit more about the dataset, and how graphs work, and the final new visualization I got is:

And now we can easily make the conclusion that our simple GNN classifies almost all nodes correctly and separates them well enough.


That is all for today!

See you tomorrow :)


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