  We introduce the feedforward neural network to attack the sign problem via
the path optimization method. The variables of integration is complexified and
the integration path is optimized in the complexified space by minimizing the
cost function which reflects the seriousness of the sign problem. For the
preparation and optimization of the integral path in multi-dimensional systems,
we utilize the feedforward neural network. We examine the validity and
usefulness of the method in the two-dimensional complex $\lambda \phi^4$ theory
at finite chemical potential as an example of the quantum field theory having
the sign problem. We show that the average phase factor is significantly
enhanced after the optimization and then we can safely perform the hybrid
Monte-Carlo method.
