Analytical derivatives of neural networks

We propose a simple recursive algorithm that allows the computation of first- and second-order derivatives with respect to inputs an arbitrary deep feed forward neural network (DFNN). The naturally incorporates parameters. To test algorithm, we apply it study quantum mechanical variational problem for few cases potentials, modeling ground-state wave function in terms DFNN.