  We demonstrate the performance of a very efficient tagger applies on
hadronically decaying top quark pairs as signal based on deep neural network
algorithms and compares with the QCD multi-jet background events. A significant
enhancement of performance in boosted top quark events is observed with our
limited computing resources. We also compare modern machine learning approaches
and perform a multivariate analysis of boosted top-pair as well as single top
quark production through weak interaction at $\sqrt{s}=$14 TeV proton-proton
Collider. The most relevant known background processes are incorporated.
Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer
Perceptron (MLP) the analysis is trained to observe the performance in
comparison with the conventional cut based and count approach.
