Articles, tutorials, and insights
A model that is 99% accurate at detecting fraud can be completely useless. It sounds like a riddle. It is not. It is one of the most common real-world...
Parathan ThiyagalingamWe have casually said "use cross-validation" for three days now. Today we finally explain it properly, along with the related question of how to find ...
Parathan ThiyagalingamDay 2 introduced overfitting. Day 3 framed it as a variance problem. Today we meet the cleanest, most asked-about tool to fix it in linear models: Reg...
Parathan ThiyagalingamThere is a saying in ML: "garbage in, garbage out." The fanciest model in the world cannot save us from bad inputs. Today we cover the most underrated...
Parathan ThiyagalingamWe have been saying "the model learns the best parameters" for six days now. Today we open that black box. Gradient Descent is the engine that powers ...
Parathan ThiyagalingamWe have casually used "accuracy" and "R²" for the last two days without thinking too carefully about them. Today we take that question seriously. Pick...
Parathan Thiyagalingam
If you have ever opened a security dashboard and felt your stomach drop at the sheer number of red and yellow rows staring back at you, this post is f...
Parathan ThiyagalingamYesterday we fit a line to predict numbers. Today, we adapt that same idea for a different kind of question: yes or no. Logistic Regression is the wor...
Parathan Thiyagalingam
Day 7 on Dense Embedding — Capturing Semantic Meaning with Vector Representations spent the whole class on the dense half of the embedding world. Toda...
Parathan Thiyagalingam