second video: use sgd for factorization. third video: modeling user sessions.

This is the sweater that proves that I am a Certified Math Nut. Okay, here’s how it works. You have to start in the bottom lefthand corner, because the mathematician in me couldn’t bear to start anywhere except where the origin would be on Cartesian coordinates. Naturally, the numbers go from left to right and from low to high.

Rails model factorization

"This paper describes a new QR factorization algorithm which is especially designed for massively parallel platforms combining parallel distributed multi-core nodes. These platforms make the present and the foreseeable future of high-performance computing. Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism)."

"Matrix Decompositions has a long history and generally centers around a set of known factorizations such as LU, QR, SVD and eigendecompositions. With the advent of new methods based on random projections and convex optimization that started in part in the compressive sensing literature, we are seeing a surge of very diverse algorithms dedicated to many different kinds of matrix factorizations with constraints based on rank, positivity, sparsity,... As a result of this large increase in interest, I have decided to keep a list of them here following the success of the big picture in compressive sensing."

"The NMF:DTU Toolbox holds a collection of Non-negative Matrix Factorization (NMF) algorithms implemented for Matlab"