  The scientific value of the next generation of large continuum surveys would
be greatly increased if the redshifts of the newly detected sources could be
rapidly and reliably estimated. Given the observational expense of obtaining
spectroscopic redshifts for the large number of new detections expected, there
has been substantial recent work on using machine learning techniques to obtain
photometric redshifts. Here we compare the accuracy of the predicted
photometric redshifts obtained from Deep Learning(DL) with the k-Nearest
Neighbour (kNN) and the Decision Tree Regression (DTR) algorithms. We find
using a combination of near-infrared, visible and ultraviolet magnitudes,
trained upon a sample of SDSS QSOs, that the kNN and DL algorithms produce the
best self-validation result with a standard deviation of {\sigma} = 0.24.
Testing on various sub-samples, we find that the DL algorithm generally has
lower values of {\sigma}, in addition to exhibiting a better performance in
other measures. Our DL method, which uses an easy to implement off-the-shelf
algorithm with no filtering nor removal of outliers, performs similarly to
other, more complex, algorithms, resulting in an accuracy of {\Delta}z < 0.1$
up to z ~ 2.5. Applying the DL algorithm trained on our 70,000 strong sample to
other independent (radio-selected) datasets, we find {\sigma} < 0.36 over a
wide range of radio flux densities. This indicates much potential in using this
method to determine photometric redshifts of quasars detected with the Square
Kilometre Array.
