Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning
Standard
Computation Graphs for AAD and Machine Learning : Part II: Adjoint Differentiation and AAD. / Savine, Antoine.
I: Wilmott, Nr. 105, 2020, s. 32–45.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Computation Graphs for AAD and Machine Learning
T2 - Part II: Adjoint Differentiation and AAD
AU - Savine, Antoine
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1002/wilm.10818
DO - 10.1002/wilm.10818
M3 - Journal article
SP - 32
EP - 45
JO - Wilmott
JF - Wilmott
SN - 1540-6962
IS - 105
ER -
ID: 250166510