  In this proceeding, we review our recent work using deep convolutional neural
network (CNN) to identify the nature of the QCD transition in a hybrid modeling
of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model
is coupled with a hadronic cascade "after-burner". As a binary classification
setup, we employ two different types of equations of state (EoS) of the hot
medium in the hydrodynamic evolution. The resulting final-state pion spectra in
the transverse momentum and azimuthal angle plane are fed to the neural network
as the input data in order to distinguish different EoS. To probe the effects
of the fluctuations in the event-by-event spectra, we explore different
scenarios for the input data and make a comparison in a systematic way. We
observe a clear hierarchy in the predictive power when the network is fed with
the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The
carefully-trained neural network can extract high-level features from pion
spectra to identify the nature of the QCD transition in a realistic simulation
scenario.
