Predictive and generative neural networks

Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148514

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DC Field	Value	Language
dc.contributor.author	Chan, Keefe	en_US
dc.date.accessioned	2021-04-29T01:59:29Z	-
dc.date.available	2021-04-29T01:59:29Z	-
dc.date.issued	2021	-
dc.identifier.citation	Chan, K. (2021). Predictive and generative neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148514	en_US
dc.identifier.uri	https://hdl.handle.net/10356/148514	-
dc.description.abstract	Machine 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 arises	en_US
dc.language.iso	en	en_US
dc.publisher	Nanyang Technological University	en_US
dc.subject	Science::Mathematics	en_US
dc.title	Predictive and generative neural networks	en_US
dc.type	Final Year Project (FYP)	en_US
dc.contributor.supervisor	Frederique Elise Oggier	en_US
dc.contributor.school	School of Physical and Mathematical Sciences	en_US
dc.description.degree	Bachelor of Science in Mathematical Sciences	en_US
dc.contributor.supervisoremail	Frederique@ntu.edu.sg	en_US
item.grantfulltext	restricted	-
item.fulltext	With Fulltext	-
Appears in Collections:	SPMS Student Reports (FYP/IA/PA/PI)

File	Description	Size	Format
MH4900 FYP Report_Keefe_Chan_U1740862L_280421.pdf Restricted Access	Generative and Predictive Neural Networks	1.2 MB	Adobe PDF	View/Open

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