  We propose an autoencoding sequence-based transceiver for communication over
dispersive channels with intensity modulation and direct detection (IM/DD),
designed as a bidirectional deep recurrent neural network (BRNN). The receiver
uses a sliding window technique to allow for efficient data stream estimation.
We find that this sliding window BRNN (SBRNN), based on end-to-end deep
learning of the communication system, achieves a significant bit-error-rate
reduction at all examined distances in comparison to previous block-based
autoencoders implemented as feed-forward neural networks (FFNNs), leading to an
increase of the transmission distance. We also compare the end-to-end SBRNN
with a state-of-the-art IM/DD solution based on two level pulse amplitude
modulation with an FFNN receiver, simultaneously processing multiple received
symbols and approximating nonlinear Volterra equalization. Our results show
that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training
fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep
learning-based systems for communication over nonlinear channels with memory,
such as the optical IM/DD fiber channel.
