50+ days of Machine Learning

 Hello :) Today is Day 145! A quick summary of today: covered  course 1 for the hackaton: Azure Kubernetes and the cloud I don't think I shared before but on Wednesday I got confirmation that I can attend  Microsoft Azura x NVIDIA hackaton in Seoul  held on Monday and Tuesday 27th and 28th of May. It will be an amazing opportunity to visit the Seoul Microsoft office, learn from Microsoft professionals and in general participate in a hackaton. On the website they mention that if we want to prepare, they recommend covering two courses: the one I did today on 1.  building and modernizing apps  and 2.  improving productivity with github and Azure for devs  - which I will do tomorrow.  Before the material, there are some nice articles that I will review further But as for the taught material, below is the outline Notes I took: K8r (Kubernetes) secrets: Store sensitive information (like passwords) safely not in plain text Encoded in base64 which makes them less visible in command outputs

 Hello :) Today is Day 144! A quick summary of today: started reading papers on using graphs for taxi demand and origin/destination predictions One of the papers my professor wrote is on  Measuring taxi ridesharing effects and its spatiotemporal pattern in Seoul, Korea  and because I am learning about GNNs from Stanford's XCS224: ML with Graphs  I remembered that one of the most popular use cases of graphs is on traffic prediction.  I read 2 papers: GNN-based Passenger Request Prediction Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling I actually found the 2nd paper from the 1st paper because it was referenced. There are these 4 models used as comparison vs the proposed GNN-based model As for the models, the 1st paper kind of goes off from the 2nd one on the idea to separate a city into grids.  and construct an OD (Origin-Destination/Adjacency) matrix based on that with requests from one cell to another Below is a pic from t