  In this paper, we apply deep learning for communication over dispersive
channels with power detection, as encountered in low-cost optical intensity
modulation/direct detection (IM/DD) links. We consider an autoencoder based on
the recently proposed sliding window bidirectional recurrent neural network
(SBRNN) design to realize the transceiver for optical IM/DD communication. We
show that its performance can be improved by introducing a weighted sequence
estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping
optimization to reduce the bit-error rate (BER) of the system. Furthermore, we
carry out a detailed comparison with classical schemes based on pulse-amplitude
modulation and maximum likelihood sequence detection (MLSD). Our investigation
shows that for a reference 42\,Gb/s transmission, the SBRNN autoencoder
achieves a BER performance comparable to MLSD, when both systems account for
the same amount of memory. In contrast to MLSD, the SBRNN performance is
achieved without incurring a computational complexity exponentially growing
with the processed memory.
