  We apply the relation between deep learning (DL) and the AdS/CFT
correspondence to a holographic model of QCD. Using a lattice QCD data of the
chiral condensate at a finite temperature as our training data, the deep
learning procedure holographically determines an emergent bulk metric as neural
network weights. The emergent bulk metric is found to have both a black hole
horizon and a finite-height IR wall, so shares both the confining and
deconfining phases, signaling the cross-over thermal phase transition of QCD.
In fact, a quark antiquark potential holographically calculated by the emergent
bulk metric turns out to possess both the linear confining part and the Debye
screening part, as is often observed in lattice QCD. From this we argue the
discrepancy between the chiral symmetry breaking and the quark confinement in
the holographic QCD. The DL method is shown to provide a novel data-driven
holographic modeling of QCD, and sheds light on the mechanism of emergence of
the bulk geometries in the AdS/CFT correspondence.
