This package represents a community effort to centralize the definition and implementation of loss functions in Julia. As such, it is a part of the JuliaML ecosystem.
The sole purpose of this package is to provide an efficient and extensible implementation of various loss functions used in Machine Learning. It is thus intended to serve as a special purpose back-end for other ML libraries that require losses to provide their functionality. To that end we provide a large list of implemented loss functions as well as an API to query their properties such as convexity. Furthermore we expose methods to compute their values, derivatives, and second derivatives for single observations as well as arbitrarily sized arrays of observations. In the case of arrays a user has the ability to define if and how element-wise results are averaged or summed over.
From an end-user's perspective one normally does not need to import this package directly. That said it can provide a decent starting point for students that are interested in investigating the properties and behaviour of loss functions.
If this is the first time you consider using LossFunctions for your machine learning related experiments or packages, make sure to check out the "Getting Started" section.
.. toctree:: :maxdepth: 2 introduction/gettingstarted
.. toctree:: :maxdepth: 2 introduction/motivation
For details on a specific aspect, see the documentation outlined below.
.. toctree:: :maxdepth: 2 losses/interface losses/distance losses/margin losses/other
.. toctree:: :hidden: :maxdepth: 2 about/acknowledgements about/license