  The purpose of this work is to examine the application of a deep learning
model in event reconstruction of neutrino interactions. The challenges faced in
event reconstruction include the placement of an accurate primary neutrino
interaction vertex which is used to support the particle track and prong
algorithms. The result of accurate primary vertex ensures all particles
involved in a neutrino interaction are included. We propose a regression-based
Convolutional Neural Network (CNN) method to predict the primary vertex of a
particle interaction. We show that with raw two-dimensional pixel map views as
input, the regression-based CNN can predict the primary vertex in all three
coordinates. This work is applied as part of the NOvA (NuMI Off-axis $\nu_e$
Appearance) near detector reconstruction efforts. The primary vertex predicted
by the regression-based CNN model shows promising results for future
applications. This deep learning method can be extended to secondary vertexing
through a Kernel Density Estimate algorithm discussed in this work.
