MatrixLab
Banni Kuytal
Linear algebra powers machine learning, graphics, and AI — yet courses often reduce it to symbol manipulation. Students memorize determinant formulas and eigenvalue algorithms without seeing what they mean geometrically. MatrixLab makes learning interactive: touch the math, and watch space, images, and performance respond in real time. Four Progressive Labs L1 · Geometry Lab — Drag basis vectors î and ĵ to warp the coordinate grid. See determinants, area scaling, reflections, and singular transforms as geometry you create. L2 · Linear Algebra Lab — Explore eigenvalues, Jordan normal form, similarity and congruence transforms, and 3D quadric surfaces from symmetric matrices. L3 · Image Filter Workshop — Edit convolution kernels and filter patterns, gradients, checkerboards, or your own photos. Presets include edge detection, sharpen, blur, and Sobel — or invent your own. L4 · Performance Engine — Animate CPU cache behavior and blocked matrix multiplication. See how memory layout drives hit rates and real-world speed. Challenges & Progress — Hands-on challenges in each lab with local progress tracking.