  The interactions of quarks and gluons are strong at non-perturbative region.
The equation of state (EoS) of a strongly-interacting quantum chromodynamics
(QCD) medium can only be studied using the first-principle lattice QCD
calculations. However, the complicated QCD EoS can be reproduced using simple
statistical formula by treating the medium as a free parton gas whose
fundamental degree of freedoms are dressed quarks and gluons called
quasi-particles, with temperature-dependent masses. We use deep neural network
and auto differentiation to solve this variational problem in which the masses
of quasi gluons, up/down and strange quarks are three unknown functions, whose
forms are represented by deep neural network. We reproduce the QCD EoS using
these machine learned quasi-particle masses, and calculate the shear viscosity
over entropy density ($\eta/s$) as a function of temperature of the hot QCD
matter.
