  We present a deep neural network representation of the AdS/CFT
correspondence, and demonstrate the emergence of the bulk metric function via
the learning process for given data sets of response in boundary quantum field
theories. The emergent radial direction of the bulk is identified with the
depth of the layers, and the network itself is interpreted as a bulk geometry.
Our network provides a data-driven holographic modeling of strongly coupled
systems. With a scalar $\phi^4$ theory with unknown mass and coupling, in
unknown curved spacetime with a black hole horizon, we demonstrate our deep
learning (DL) framework can determine them which fit given response data.
First, we show that, from boundary data generated by the AdS Schwarzschild
spacetime, our network can reproduce the metric. Second, we demonstrate that
our network with experimental data as an input can determine the bulk metric,
the mass and the quadratic coupling of the holographic model. As an example we
use the experimental data of magnetic response of a strongly correlated
material Sm$_{0.6}$Sr$_{0.4}$MnO$_3$. This AdS/DL correspondence not only
enables gravity modeling of strongly correlated systems, but also sheds light
on a hidden mechanism of the emerging space in both AdS and DL.
