  Resolving the phase structure of neutral hydrogen (HI) is crucial for
understanding the life cycle of the interstellar medium (ISM). However,
accurate measurements of HI temperature and density are limited by the
availability of background continuum sources for measuring HI absorption. Here
we test the use of deep learning for extracting HI properties over large areas
without optical depth information. We train a 1D convolutional neural network
using synthetic observations of 3D numerical simulations of the ISM to predict
the fraction of cold neutral medium (f_CNM) and the correction to the
optically-thin HI column density for optical depth (R_HI) from $21\rm\,cm$
emission alone. We restrict our analysis to high Galactic latitudes ($|b|>30
deg), where the complexity of spectral line profiles is minimized. We verify
that the network accurately predicts f_CNM and R_HI by comparing the results
with direct constraints from 21 cm absorption. By applying the network to the
GALFA-HI survey, we generate large-area maps of f_CNM and R_HI. Although the
overall contribution to the total HI column of cold neutral medium (CNM)-rich
structures is small (~5%), we find that these structures are ubiquitous. Our
results are consistent with the picture that small-scale structures observed in
21 cm emission aligned with the magnetic field are dominated by CNM. Finally,
we demonstrate that the observed correlation between HI column density and dust
reddening (E(B-V)) declines with increasing R_HI, indicating that future
efforts to quantify foreground Galactic E(B-V) using HI, even at high
latitudes, should increase fidelity by accounting for HI phase structure.
