All Katas - ML Katas

PCA from First Principles

this year hard (>1 hr) | linear-algebra PCA dim-reduction eigenvalues eigenvectors

Principal Component Analysis (PCA) is a fundamental dimensionality reduction technique. It works by transforming the data into a new coordinate system such that the greatest variance by any...

SVD for Image Compression

this year hard (>1 hr) | compression linear-algebra dim-reduction SVD image-processing

Singular Value Decomposition (SVD) is a powerful matrix factorization technique with numerous applications, including dimensionality reduction, noise reduction, and data compression. Any real $m...

Matrix Multiplication and Efficiency

this year medium (<30 mins) | optimization matrix-multiplicatio linear-algebra algorithms computational-comp

Matrix multiplication is a fundamental operation in linear algebra and a cornerstone of deep learning. Given two matrices $A$ (size $m \times k$) and $B$ (size $k \times n$), their product $C =...