50+ days of Machine Learning

 Hello :) Today is Day 96! A quick summary of today: covered the GNN training pipeline from Stanford's XCS224W: ML with Graphs applied for a few internships this summer + found theforage My notes from the training pipeline lectures Covered topics: graph augmentation, feature augmentation, training GNNs on a node-level, edge-level and graph-level, pooling for graph-level tasks, DiffPool, supervised, unsupervised and self-supervised learning, loss functions, evaluation metrics, splitting data Looking for opportunities this summer I am in South Korea, but I still have the right to work in the UK, so I decided to go on Bright Network to look for data analytics/science internships and saw some good opportunities. I hope that if a company shows interest they would do an online interview. In addition, I found TheForage  which provides free virtual interships. Though short, they could give some interesting perspectives into the work life of a software engineer or investment banker (th...

 Hello :) Today is Day 95! A quick summary of today: learned how a GNN layer is constructed and operates ( XCS224W: ML with Graphs ) became a fellow of the Royal Statistical Society  (I thought I already was) Lecture 2.2 GNN Design Space Covered topics: Designing a single layer of a GNN, message computation, aggregation, GCN, GraphSAGE, GAT, attention and multi-head attention in graphs, stacking GNN layers, the problem of over-smoothing, shallow GNNs, using skip connections Becoming a fellow of the Royal Statistical Society I was registered as a student in the RSS and I thought I was like a regular member, but apparently I needed to register as a proper 'Fellow'. Going forward I see there are events that they organise, and have some courses on statistics through which I can climb the pyramid Also, for a bit I tried making a discord server for study groups and discussions between students of the XCS224W course, so far noone wanted to join. I am not sure how they usually do...

 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 g...

 Hello :) Today is Day 93! A quick summary of today: covered lecture 1.2 Node embeddings on XCS224W: ML with Graphs from  Probabilistic Machine Learning: An Introduction , covered: chapter 5: Decision theory chapter 6: information theory chapter 7: linear algebra First, my notes from lecture 1.2 Node embeddings for graphs Covered topics: node embeddings: encoder and decoder, random walk, unsupervised feature learning, random walk optimization, negative sampling, node2vec, anonymous walks, learning walk embeddings Next, from Probabilistic ML: An introduction by Kevin Murphy Chapter 5: Decision theory Covered topics: classification problems, ROC curve, Precision-Recall curves, F-scores, Regression problems Chapter 6: Information theory Covered topics: entropy, entropy of discrete random variables, cross entropy, conditional entropy, perplexity Chapter 7: Linear algebra Covered topics: notations(vectors, matrices, tensors, vector spaces, linear map, properties), matrix multiplica...