50+ days of Machine Learning

 Hello :) Today is Day 94! A quick summary of today: Finished the last part of Traditional ML methods for Graphs: Link analysis page rank random walks and embeddings Did assignment CoLab 1: Learning Node Embeddings Covered first part of Module 2: Intro to GNNs Today I continued with the coverage of XCS224W: ML with Graphs My notes for Link analysis page rank random walks and embeddings Covered topics: PageRank, Matrix Formulation, Power iteration method, Solutions to dead-ends and spider traps, Personalised PageRank, Random walk with restarts, Using Matrix factorization to express node embeddings based on random walks Assignment 1: Learning Node Embeddings  We are not allowed to share any of the code, and I really do not want to risk anything, so I will just say, similar colabs can be found on the course's main webpage . But I spend a lot of time to understand each line of code that I wrote, and how theory from Module 1 on node embeddings is applied to practice. Also, I got 30/30 ^