  Interest in deep learning in collider physics has been growing in recent
years, specifically in applying these methods in jet classification, anomaly
detection, particle identification etc. Among those, jet classification using
neural networks is one of the well-established areas. In this review, we
discuss different tagging frameworks available to tag boosted objects,
especially boosted Higgs boson and top quark, at the Large Hadron Collider
(LHC). Our aim is to study the interplay of traditional jet substructure based
methods with the state-of-the-art machine learning ones. In this methodology,
we would gain some interpretability of those machine learning methods, and
which in turn helps to propose hybrid taggers relevant for tagging of those
boosted objects belonging to both Standard Model (SM) and physics beyond the
SM.
