Experiments bench
The Lab
Favorite concepts from my Emory coursework and self-study, rebuilt as interactive demos to make them intuitive for anyone learning them.
Optimization
Gradient Descent
Drop a marble on a loss surface and watch it roll. Race SGD, momentum, and Adam.
Why the learning rate is the first knob you touch — and the first one that ruins a run.
Learning-Rate Schedules
Constant, step decay, cosine annealing, and warmup — side by side.
Big steps early, small steps late: why schedules beat any single learning rate.
Classic models
k-Means Clustering
Scatter your own points, pick k, and step through assign and update by hand.
How a good algorithm still lands in a bad answer when it starts in the wrong place.
SVM & the Kernel Trick
Drag points around a margin, then lift a tangle into a plane where a line works.
Why the widest street between classes generalizes — and how kernels make one exist.
Linear vs. Logistic
Place points and watch the fit chase them, residuals drawn in pencil.
The same machinery predicting a number versus predicting a probability.