  Identifying symmetries in data sets is generally difficult, but knowledge
about them is crucial for efficient data handling. Here we present a method how
neural networks can be used to identify symmetries. We make extensive use of
the structure in the embedding layer of the neural network which allows us to
identify whether a symmetry is present and to identify orbits of the symmetry
in the input. To determine which continuous or discrete symmetry group is
present we analyse the invariant orbits in the input. We present examples based
on rotation groups $SO(n)$ and the unitary group $SU(2).$ Further we find that
this method is useful for the classification of complete intersection
Calabi-Yau manifolds where it is crucial to identify discrete symmetries on the
input space. For this example we present a novel data representation in terms
of graphs.
