  Using techniques from deep learning (cf. [B\"uh+19]), we show that neural
networks can be trained successfully to replicate the modified payoff functions
that were first derived in the context of partial hedging by [FL00]. Not only
does this approach better accommodate the realistic setting of hedging in
discrete time, it also allows for the inclusion of transaction costs as well as
general market dynamics.
