Our second R&D Report, Probabilistic Methods for Realtime Streams, has gone out! In this report, we explored probabilistic algorithms for machine learning on potentially large realtime streams of data with efficient CPU and memory usage.
Our prototype demonstrated these algorithms running on large amounts of social conversation data. More on that soon.
More from the Blog
Mar 31 2015
The Hoff stopped by Fast Forward Labs!
Apr 6 2015
by — It’s restyled blog day here at Fast Forward Labs! Having wrapped up our second report, I (Grant, Fast Forward Labs designer-dev) am now spending some time putting our web presence in order. The real big piece of that is a revamped Fast Forward website, which should be out next week, but as a warm-up today I worked on getting our blog into shape. I wanted to keep things clean ...
Jan 29 2019
by — UMAP explorer: an interactive visualization of the MNIST data set We’re in the middle of work on our next report, Learning with Limited Labeled Data, and the accompanying prototype. For the prototype’s front-end we wanted to be able visualize and explore the embedding of a large image data set. Once you get into the tens of thousands of points, this can be a challenge to do in the browser. T...