  Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new
windows on the Universe, allowing us to infer physical properties for tens of
millions of galaxies. Machine learning methods are increasingly becoming the
most efficient tools to handle this enormous amount of data, because they are
often faster and more accurate than traditional methods. We investigate how
well redshifts, stellar masses, and star-formation rates (SFR) can be measured
with deep learning algorithms for observed galaxies within data mimicking the
Euclid and Rubin/LSST surveys. We find that Deep Learning Neural Networks and
Convolutional Neutral Networks (CNN), which are dependent on the parameter
space of the training sample, perform well in measuring the properties of these
galaxies and have a better accuracy than methods based on spectral energy
distribution fitting. CNNs allow the processing of multi-band magnitudes
together with $H_{\scriptscriptstyle\rm E}$-band images. We find that the
estimates of stellar masses improve with the use of an image, but those of
redshift and SFR do not. Our best results are deriving i) the redshift within a
normalised error of less than 0.15 for 99.9$\%$ of the galaxies with S/N>3 in
the $H_{\scriptscriptstyle\rm E}$-band; ii) the stellar mass within a factor of
two ($\sim0.3 \rm dex$) for 99.5$\%$ of the considered galaxies; iii) the SFR
within a factor of two ($\sim0.3 \rm dex$) for $\sim$70$\%$ of the sample. We
discuss the implications of our work for application to surveys as well as how
measurements of these galaxy parameters can be improved with deep learning.
