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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskning

  • Antoine Savine
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.
OriginalsprogEngelsk
TidsskriftWilmott
Udgave nummer105
Sider (fra-til)32–45
ISSN1540-6962
DOI
StatusUdgivet - 2020

ID: 250166510