Let’s build open source tensor libraries for data science

[A version of this post appears on the O’Reilly Radar blog.] Tensor methods for machine learning are fast, accurate, and scalable, but we’ll need well-developed libraries. Data scientists frequently find themselves dealing with high-dimensional feature spaces. As an example, text mining usually involves vocabularies comprised of 10,000+ different words. Many analytic problems involve linear algebra,Continue reading “Let’s build open source tensor libraries for data science”

Bits from the Data Store

Semi-regular field notes from the world of data (gathered from Scifoo 2014): Filtergraph and the power of visual exploration: A web-based tool for exploring high-dimensional data sets, Filtergraph came out of the lab of Astrophysicist Keivan Stassun. It has helped researchers make several interesting discoveries including a paper (that appeared in Nature) on a techniqueContinue reading “Bits from the Data Store”

How signals, geometry, and topology are influencing data science

[A version of this post appears on the O’Reilly Strata blog.] I’ve been noticing unlikely areas of mathematics pop-up in data analysis. While signal processing is a natural fit, topology, differential and algebraic geometry aren’t exactly areas you associate with data science. But upon further reflection perhaps it shouldn’t be so surprising that areas thatContinue reading “How signals, geometry, and topology are influencing data science”