50+ days of Machine Learning

 Hello :) Today is Day 157! A quick summary of today: read about design choices for GNNs started reading a book about MLOps on manning.com registered for Korea summer workshop on causal inference Design choices for Graph Neural Networks [ arxiv ] I saw this paper from the last short lecture from XCS224W: ML with graphs, it looks into how different design choices affect a GNN model's performance. It caugt my eye as some of the findings as to what is useful could be tested/applied in my work/research at the lab.  Below are some of the interesting findings: For example, having batch norm is always better more than not having it; no dropout seems to perform better too, prelu activation seems the best, and using sum as an aggregation function outperforms mean and max.  Design a Machine Learning System (From Scratch) [ book ] The book will teach me how to: Build an ML Platform Build and Deploy ML Pipelines Extend the ML Platform using various tools depending on use cases Implement differ