mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and function as a "swiss army knife" for machine learning researchers. The mlpack development website can be found at http://mlpack.org.
mlpack uses the Armadillo C++ matrix library (http://arma.sourceforge.net) for general matrix, vector, and linear algebra support. mlpack also uses the program_options, math_c99, and unit_test_framework components of the Boost library, and optionally uses libbfd and libdl to give backtraces when compiled with debugging symbols on some platforms.
This documentation is API documentation similar to Javadoc. It isn't necessarily a tutorial, but it does provide detailed documentation on every namespace, method, and class.
Each mlpack namespace generally refers to one machine learning method, so browsing the list of namespaces provides some insight as to the breadth of the methods contained in the library.
To generate this documentation in your own local copy of mlpack, you can simply use Doxygen, from the root directory of the project:
mlpack provides several executables so that mlpack methods can be used without any need for knowledge of C++. These executables are all self-documented, and that documentation can be accessed by running the executables with the '-h' or '–help' flag.
A full list of executables is given below:
A few short tutorials on how to use mlpack are given below.
Tutorials on specific methods are also available.
The following methods are included in mlpack:
mlpack contributors include: