April 2013

As some of you know I have been working on a machine learning library for .NET called numl. The main purpose of the library is to abstract away some of the mundane issues surrounding setting up the learning problem in the first place. Additionally sometimes the math in machine learning seems to be a bit daunting (some of it is indeed daunting) so the library allows you to either get into the math or trust that these things are implemented and run correctly.

In order to facilitate this type of abstraction I came to realize that the best way to bridge this gap was to use constructions that most would have already either used or understood: classes. The learning problem, as I understood it, was taking a set of things and trying to learn a way to predict a particular aspect of these things. The best approach therefore was to allow for an easy way to markup these things (or classes) in order to produce an efficient technique for setting up the learning problem.

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