  We release photometric redshifts, reaching $\sim$0.7, for $\sim$14M galaxies
at $r\leq 20$ in the 11,500 deg$^2$ of the SDSS north and south galactic caps.
These estimates were inferred from a convolution neural network (CNN) trained
on $ugriz$ stamp images of galaxies labelled with a spectroscopic redshift from
the SDSS, GAMA and BOSS surveys. Representative training sets of $\sim$370k
galaxies were constructed from the much larger combined spectroscopic data to
limit biases, particularly those arising from the over-representation of
Luminous Red Galaxies. The CNN outputs a redshift classification that offers
all the benefits of a well-behaved PDF, with a width efficiently signaling
unreliable estimates due to poor photometry or stellar sources. The dispersion,
mean bias and rate of catastrophic failures of the median point estimate are of
order $\sigma_{\rm MAD}=0.014$, <$\Delta z_{\rm norm}$>$=0.0015$, $\eta(|\Delta
z_{\rm norm}|>0.05)=4\%$ on a representative test sample at $r<19.8$,
out-performing currently published estimates. The distributions in narrow
intervals of magnitudes of the redshifts inferred for the photometric sample
are in good agreement with the results of tomographic analyses. The inferred
redshifts also match the photometric redshifts of the redMaPPer galaxy clusters
for the probable cluster members. The CNN input and output are available at:
https://deepdip.iap.fr/treyer+2023.
