  Most state-of-the-art machine learning techniques revolve around the
optimisation of loss functions. Defining appropriate loss functions is
therefore critical to successfully solving problems in this field. We present a
survey of the most commonly used loss functions for a wide range of different
applications, divided into classification, regression, ranking, sample
generation and energy based modelling. Overall, we introduce 33 different loss
functions and we organise them into an intuitive taxonomy. Each loss function
is given a theoretical backing and we describe where it is best used. This
survey aims to provide a reference of the most essential loss functions for
both beginner and advanced machine learning practitioners.
