Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148514
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChan, Keefeen_US
dc.date.accessioned2021-04-29T01:59:29Z-
dc.date.available2021-04-29T01:59:29Z-
dc.date.issued2021-
dc.identifier.citationChan, K. (2021). Predictive and generative neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148514en_US
dc.identifier.urihttps://hdl.handle.net/10356/148514-
dc.description.abstractMachine learning applications based on neural networks have been flourishing over the years. In this report, we explore how to generate and predict random variables using neural networks, starting from well known methods, namely the inverse transform method and maximum likelihood techniques, then evolving towards scenarios where the need of predictive and generative neural networks arisesen_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectScience::Mathematicsen_US
dc.titlePredictive and generative neural networksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorFrederique Elise Oggieren_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Mathematical Sciencesen_US
dc.contributor.supervisoremailFrederique@ntu.edu.sgen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
MH4900 FYP Report_Keefe_Chan_U1740862L_280421.pdf
  Restricted Access
Generative and Predictive Neural Networks1.2 MBAdobe PDFView/Open

Page view(s)

169
Updated on May 17, 2022

Download(s) 50

42
Updated on May 17, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.