Unified and efficient Machine Learning.

Shogun seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.

We have various ideas for this years Summer of Code. This year, our focus is not on adding new ML algorithms, but on rather software engineering driven goals, in particular

  1. installation & usability
  2. framework re-factoring & clean-ups
  3. efficiency & benchmarks

Please read how to get involved with us before applying. Then please use the scheme shown below for your student application. If you have any questions, ask on the mailing list (shogun-list@shogun-toolbox.org, please note that you have to be subscribed in order to post) or IRC #shogun on freenode.

lightbulb_outline View ideas list

Technologies

  • c++
  • python
  • swig
  • cmake

Topics

  • Science and Medicine
  • machine learning
  • statistics
  • fast algorithms
  • software engineering
  • bioinformatics
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Shogun Machine Learning Toolbox 2016 Projects

  • Saurabh7
    Fundamental ML: The usual suspects
    This project aims to improve Shogun’s implementation of the fundamental algorithms which are necessary and form the base of any machine learning...
  • Sanuj Sharma
    New Parameter Framework and Plugin Based Architecture for Shogun
    Shogun is an amazing machine learning toolbox with good set of algorithms available. Since the project is old, the code base is huge and it's old. A...
  • OXPHOS
    The Shogun Detox – GSoC 2016 Proposal
    As a powerful machine learning toolkit, Shogun was achieved by the efforts of many developers. However, this also implicates the trouble with Shogun:...
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2016