  In these proceedings we present lattice gauge equivariant convolutional
neural networks (L-CNNs) which are able to process data from lattice gauge
theory simulations while exactly preserving gauge symmetry. We review aspects
of the architecture and show how L-CNNs can represent a large class of gauge
invariant and equivariant functions on the lattice. We compare the performance
of L-CNNs and non-equivariant networks using a non-linear regression problem
and demonstrate how gauge invariance is broken for non-equivariant models.
