Co-author Brian Huge and I were interviewed by Risk Magazine last week and got a chance to present differential ML and related algorithms in non-technical terms. The podcast is publicly available https://www.risk.net/cutting-edge/views/7880346/podcast-huge-and-savine-on-turbo-charging-derivatives-pricing
Differential regression illustrates the main ideas of differential machine learning, and demonstrates its power in a very simple context. We posted a small article with code on Google Colab, run it here:
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.
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Antoine Savine 