Unified and efficient Machine Learning

Shogun implements a wide range of unified and efficient machine learning algorithms. The library allows easy and seamless combination of multiple data representations, algorithm classes, and general-purpose tools. This enables both rapid prototyping of data pipelines and quick implementations of new algorithms. Shogun combines modern software architecture in C++ with an efficient low-level computing backend and cutting-edge algorithm implementations to solve large-scale machine learning problems. The automatically generated interfaces allow to use Shogun from many modern high-level languages under a unified API.

We value and focus on our community of developers and users, encouraging and catalyzing learning experiences in machine learning, scientific computing, software-engineering, and project organisation.

Starting in 1999, the project has come a long way in terms of code-base, developer team, and use-cases. The current project focus continues being not on adding new ML algorithms, but rather on improving the usability in particular

  1. modernization of the framework
  2. applications & user experiences
  3. algorithm efficiency & benchmarks
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  • python
  • c++
  • data science
  • machine learning
  • matlab


  • Science and Medicine
  • machine learning
  • scientific computing
  • software engineering
  • user experience
  • data science
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Shogun 2020 Projects

  • Yuhui Liu
    Improving the user experience
    Refactor the base class Machine API, Add Composite and so on.
  • Tej Sukhatme
    Web Tool for Disease Estimation
    This project aims at showcasing Shogun’s capabilities as well as making a web tool and an API that everyone can use so as to be able to successfully...