  Understanding the star-formation properties of galaxies as a function of
cosmic epoch is a critical exercise in studies of galaxy evolution.
Traditionally, stellar population synthesis models have been used to obtain
best fit parameters that characterise star formation in galaxies. As multiband
flux measurements become available for thousands of galaxies, an alternative
approach to characterising star formation using machine learning becomes
feasible. In this work, we present the use of deep learning techniques to
predict three important star formation properties -- stellar mass, star
formation rate and dust luminosity. We characterise the performance of our deep
learning models through comparisons with outputs from a standard stellar
population synthesis code.
