  We provide a deep Boltzmann machine (DBM) for the AdS/CFT correspondence.
Under the philosophy that the bulk spacetime is a neural network, we give a
dictionary between those, and obtain a restricted DBM as a discretized bulk
scalar field theory in curved geometries. The probability distribution as
training data is the generating functional of the boundary quantum field
theory, and it trains neural network weights which are the metric of the bulk
geometry. The deepest layer implements black hole horizons, and an employed
regularization for the weights is an Einstein action. A large $N_c$ limit in
holography reduces the DBM to a folded feed-forward architecture. We also
neurally implement holographic renormalization into an autoencoder. The DBM for
the AdS/CFT may serve as a platform for studying mechanisms of spacetime
emergence in holography.
