DLib: Library for Machine Learning

DLib is an open source C++ library implementing a variety of machine learning algorithms, including classification, regression, clustering, data transformation, and structured prediction.

DLib is an open source modern C++ library implementing many machine learning algorithms and supporting functionality like threading and networking.

DLib-ml implements numerous machine learning algorithms:
  • SVMs,
  • K-Means clustering,
  • Bayesian Networks,
  • and many others.
DLib also features utility functionality including
  • Threading,
  • Networking,
  • Numerical Algorithms,
  • Image Processing,
  • and Data Compression and Integrity algorithms.
DLib includes extensive unit testing coverage and examples using the library. Every class and function in the library is documented. This documentation can be found on the library's home page. DLib provides a good framework for developing machine learning applications in C++.

DLib is much like DMTL in that it provides a generic high-performance machine learning toolkit with many different algorithms, but DLib is more recently updated and has more examples. DLib also contains much more supporting functionality.

What makes DLib unique is that it is designed for both research use and creating machine learning applications in C++.

The official paper describing the machine learning part of the toolkit can be found here.

Download DLib here

DLib works on Windows, Linux, and OS X.

DLib is licensed under the Boost Software License.

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