• 01 Distribution Visualizer PDF/CDF visualizer for Normal, Uniform, Exponential, Laplace, Dirac, and Mixture distributions. Statistics
• 02 Unsupervised Supervised Learning Step through a demo to show how to solve a supervised classification problem using unsupervised density estimation. Machine learning
• 03 Linear Regression Regularization Interactive comparison of several linear regression regularization methods and their results. Machine learning
• 04 Gradient Descent Interactive sandbox comparing full-batch, mini-batch, and stochastic gradient descent, and SGD, momentum, AdaGrad, RMSProp, and Adam optimizers, on a shared loss surface. Machine learning
• 05 Estimation: Bias, Variance, MSE, MLE Notes on point/function estimation, standard error, minimizing MSE, consistency, and likelihood objectives. Statistics
• 06 Bayesian Inference Priors, posteriors, conjugacy, MAP, posterior predictive, and approximate inference (MCMC/VI). Statistics
• 07 Principal Component Analysis Interactive walkthrough of PCA via SVD, principal directions, covariance eigendecomposition, and reconstruction. Machine learning
• 08 Fourier Image Decomposition DFT periodicity assumption, low-pass filtering, the Gibbs phenomenon, and why non-periodic images produce checkerboard artifacts. Signals
• 09 Manifold Learning An animated Isomap walkthrough plus a step-by-step side-by-side comparison of manifold learning algorithms on synthetic datasets or your own CSV. Machine learning
• 10 Attention Single-head scaled dot-product attention worked end to end: a clickable pipeline diagram where every step expands into the real numbers, from Q/K/V projections through masking, softmax, and the weighted sum. Machine learning