Posts

Exploring the potential in low-rank approximations for the compression of neural networks and the latest applications of matrix decomposition to LLMs at scale.

Measures of risk, dependence, and the simulation of market data for statistical arbitrage.

Eigenvalues and eigenvectors remain among the most important concepts in the study of feature extraction, behavior of dynamical systems, matrix factorizations, and many more data science topics. A brief …