Deep Learning is picking momentum in Quantitative Finance, outside the obvious application to the prediction of asset prices (where to my knowledge it is not particularly effective) and spreading into the more serious application area of option pricing and risk management.
These two recent papers clearly demonstrate the benefits of DL as a pricing technology alternative to the classical FDM and Monte-Carlo in certain contexts:
These posts also explore the ideas of 'deep calibration' (application of DL to the calibration of financial models in a particularly interesting form) and 'deep analytics' (using DL to resolve the conundrum of revaluation in the context of regulatory simulations):
I should add that those two posts only constitute early explorations. Those are extremely interesting and high potential ideas, but serious implementation challenges must be overcome to put them in production.