  While the available set of Gamma-ray Burst (GRB) data with known redshift is
currently limited, a much larger set of GRB data without redshift is available
from different instruments. This data includes well-measured prompt gamma-ray
flux and spectral information. We estimate the redshift of a selection of these
GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques
that are based on spectral parameters. We find that Deep Neural Networks with
Random Forest models employing non-linear relations among input parameters can
reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the
distribution of GRBs with spectroscopic redshift. Furthermore, we find that the
pseudo-redshift samples of GRBs satisfy (i) Amati relation between the peak
photon energy of the time-averaged energy spectrum in the cosmological rest
frame of the GRB ${E}_{\rm i, p}$ and the isotropic-equivalent radiated energy
${E}_{\rm iso}$ during the prompt phase; and (ii) Yonetoku relation between
${E}_{\rm i, p}$ and isotropic-equivalent luminosity ${L}_{\rm iso}$, both
measured during the peak flux interval.
