Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD

Research output: Contribution to journalJournal articleResearch

Second in a series of three articles with code, exploring the notion of computation graph, with words, mathematics and code, and application in Machine Learning and finance to compute a vast number of derivative sensitivities with spectacular speed and accuracy.
Original languageEnglish
Issue number105
Pages (from-to)32–45
Publication statusPublished - 2020

ID: 250166510