  Higgs boson pair production is well known to probe the structure of the
electroweak symmetry breaking sector. We illustrate using the gluon-fusion
process $pp \to H \to h h \to (b\bar b) (b\bar b)$ in the framework of
two-Higgs-doublet models and how the machine learning approach (three-stream
convolutional neural network) can substantially improve the signal-background
discrimination and thus improves the sensitivity coverage of the relevant
parameter space. We show that such $gg \to hh \to b \bar b b\bar b$ process can
further probe the currently allowed parameter space by HiggsSignals and
HiggsBounds at the HL-LHC. The results for Types I to IV are shown.
