  We make the connection between certain deep learning architectures and the
renormalisation group explicit in the context of QCD by using a deep learning
network to construct a toy parton shower model. The model aims to describe
proton-proton collisions at the Large Hadron Collider. A convolutional
autoencoder learns a set of kernels that efficiently encode the behaviour of
fully showered QCD collision events. The network is structured recursively so
as to ensure self-similarity, and the number of trained network parameters is
low. Randomness is introduced via a novel custom masking layer, which also
preserves existing parton splittings by using layer-skipping connections. By
applying a shower merging procedure, the network can be evaluated on unshowered
events produced by a matrix element calculation. The trained network behaves as
a parton shower that qualitatively reproduces jet-based observables.
