The jQuery Mobile sample on the WebSharper site has been out for quite some time now, but it fails to mention an important detail on how to run it standalone. Indeed, copying it straight into a new WebSharper ASP.NET project produces an empty page (which is not empty, only jQuery Mobile fails to kick in to decorate it into a full application), which can be a discouraging start for anyone looking into using jQuery Mobile for building mobile applications with F# and WebSharper. [...]

This post continues my journey converting the Python samples from Machine Learning in Action into F#. On the program today: chapter 7, dedicated to AdaBoost. This is also the last chapter revolving around classification. After almost 6 months spending my week-ends on classifiers, I am rather glad to change gears a bit! The idea behind the algorithm Algorithm outline AdaBoost is short for “Adaptative Boosting”. Boosting is based on a very common-sense idea: instead of trying to find one perfect classifier t[...]

This is the continuation of my series converting the samples found in Machine Learning in Action from Python to F#. After starting on a nice and steady pace, I hit a speed bump with Chapter 6, dedicated to the Support Vector Machine algorithm. The math is more involved than the previous algorithms, and the original Python implementation is very procedural , which both slowed down the conversion to a more functional style. Anyways, I am now at a good point to share progress. The current version uses Sequent[...]