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.
Convolution and filters
Slide a 3 × 3 kernel over an image: blur, sharpen, edge detection. Edit the weights and follow the computation of one pixel, term by term.
Haar wavelets (DWT)
Decompose an image into LL, HL, LH and HH sub-bands over several levels, drop the small coefficients and measure what the reconstruction loses.
Pathfinding: BFS, DFS, Dijkstra, A*
Four algorithms explore the same grid. Draw walls, add costly terrain and compare the visited cells and the cost of the path.