Hello :) Today is Day 90!
A quick summary of today:
- covered the first chapter of the Foundations part of Probabilistic Machine Learning: An Introduction
- set up my environment and covered the basics from the 0th colab for Stanford’s XCS224W: ML with Graphs
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 :)