The third and final part of my paper “Computation graphs for AAD and Machine Learning part III: Application to Derivatives Sensitivities” was just published in Wilmott, the third and final in a series of three articles with code dedicated to AAD and Computational Finance in general. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.
The first parts were published in Wilmott November 2019 issue and Wilmott January 2020 issue.
Antoine Savine 