  This paper proposes a conjugate-gradient-based Adam algorithm blending Adam
with nonlinear conjugate gradient methods and shows its convergence analysis.
Numerical experiments on text classification and image classification show that
the proposed algorithm can train deep neural network models in fewer epochs
than the existing adaptive stochastic optimization algorithms can.
