  High $p_T$ Higgs production at hadron colliders provides a direct probe of
the internal structure of the $gg \to H$ loop with the $H \to b\bar{b}$ decay
offering the most statistics due to the large branching ratio. Despite the
overwhelming QCD background, recent advances in jet substructure have put the
observation of the $gg\to H \to b\bar{b}$ channel at the LHC within the realm
of possibility. In order to enhance the sensitivity to this process, we develop
a two stream convolutional neural network, with one stream acting on jet
information and one using global event properties. The neural network
significantly increases the discovery potential of a Higgs signal, both for
high $p_T$ Standard Model production as well for possible beyond the Standard
Model contributions. Unlike most studies for boosted hadronically decaying
massive particles, the boosted Higgs search is unique because double
$b$-tagging rejects nearly all background processes that do not have two hard
prongs. In this context --- which goes beyond state-of-the-art two-prong
tagging --- the network is studied to identify the origin of the additional
information leading to the increased significance. The procedures described
here are also applicable to related final states where they can be used to
identify additional sources of discrimination power that are not being
exploited by current techniques.
