Baikal-GVD is a large (∼ 1 km3) underwater neutrino telescope
installed in the fresh waters of Lake Baikal. The deep lake water environment
is pervaded by background light, which produces detectable signals in the
Baikal-GVD photosensors. We introduce a neural network for an efficient
separation of these noise hits from the signal ones, stemming from the
propagation of relativistic particles through the detector. The neural network
has a U-net like architecture and employs temporal (causal) structure of
events. On Monte-Carlo simulated data, it reaches 99% signal purity (precision)
and 98% survival efficiency (recall). The benefits of using neural network for
data analysis are discussed, and other possible architectures of neural
networks, including graph based, are examined