  A machine learning technique with two-dimension convolutional neural network
is proposed for detecting exoplanet transits. To test this new method, five
different types of deep learning models with or without folding are constructed
and studied. The light curves of the Kepler Data Release 25 are employed as the
input of these models. The accuracy, reliability, and completeness are
determined and their performances are compared. These results indicate that a
combination of two-dimension convolutional neural network with folding would be
an excellent choice for the future transit analysis.
