Our previous roundup of machine learning resources touched mlpack, a C++-based machine learning library originally rolled out in 2011 and designed for “scalability, speed, and ease-of-use,” according to the library’s creators. Implementing mlpack can be done through a cache of command-line executables for quick-and-dirty, “black box” operations, or with a C++ API for more sophisticated work.
The 2.0 version has lots of refactorings and new features, including many new kinds of algorithms, and changes to existing ones to speed them up or slim them down. For example, it ditches the Boost library’s random number generator for C++11’s native random functions.
One long-standing disadvantage is a lack of bindings for any language other than C++, meaning users of everything from R to Python can’t make use of mlpack unless someone rolls their own wrappers for said languages. Work’s been done to add MATLAB support, but projects like this tend to enjoy greater uptake when they’re directly useful in the major environments where machine learning work takes place.