Gradient descent
Pick a function, a starting point and a learning rate, then watch the point walk downhill: convergence, oscillations, divergence, local minima.
7 demos
Interactive lab
Demos that really compute: every figure is produced in your browser by the algorithm it explains. Change the parameters, observe, understand.
Optimizers: gradient, momentum, Adam
Three optimizers start from the same point on a two-variable surface. Compare their trajectories over the level curves: zigzag, momentum, adaptive step.
K-Means step by step
Assign, update, assign… Follow the centroids until convergence, and discover where K-Means goes wrong: bad start, stretched clusters, rings.
Linear regression
Move the points and watch the least squares line follow. Then learn the same line by gradient descent and compare.