  The objective of this research was to evaluate and gain experience with
application of two methods used for pricing and sensitivity analysis of exotic
financial derivative instruments, namely, automatic adjoint differentiation
(AAD) and deep learning. The work was inspired by publication of Danske Bank
quantitative analysts Antoine Savine and Brian Huge in which the authors
introduced a novel approach to building extremely efficient pricing and risk
approximators for arbitrary financial derivative instruments.

Disordered Systems and Neural Networks