  We investigate end-to-end optimized optical transmission systems based on
feedforward or bidirectional recurrent neural networks (BRNN) and deep
learning. In particular, we report the first experimental demonstration of a
BRNN auto-encoder, highlighting the performance improvement achieved with
recurrent processing for communication over dispersive nonlinear channels.
