  We have employed deep neural network, or deep learning to predict the flux
and the shape of the broad Ly$\alpha$ emission lines in the spectra of quasars.
We use 17870 high signal-to-noise ratio (SNR > 15) quasar spectra from the
Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14) to train the model and
evaluate its performance. The SiIV, CIV, and CIII] broad emission lines are
used as the input to the neural network, and the model returns the predicted
Ly$\alpha$ emission line as the output. We found that our neural network model
predicts quasars continua around the Ly$\alpha$ spectral region with $\sim$6 -
12% precision and $\lesssim$1% bias. Our model can be used to estimate the HI
column density of eclipsing and ghostly damped Ly$\alpha$ (DLA) absorbers as
the presence of the DLA absorption in these systems strongly contaminates the
flux and the shape of the quasar continuum around Ly$\alpha$ spectral region.
The model could also be used to study the state of the intergalactic medium
during the epoch of reionization.
