Predictive and generative neural networks

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 e...

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Main Author: Chan, Keefe
Other Authors: Frederique Elise Oggier
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148514
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1485142023-02-28T23:14:18Z Predictive and generative neural networks Chan, Keefe Frederique Elise Oggier School of Physical and Mathematical Sciences Frederique@ntu.edu.sg Science::Mathematics 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 Bachelor of Science in Mathematical Sciences 2021-04-29T01:59:29Z 2021-04-29T01:59:29Z 2021 Final Year Project (FYP) Chan, K. (2021). Predictive and generative neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148514 https://hdl.handle.net/10356/148514 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics
spellingShingle Science::Mathematics
Chan, Keefe
Predictive and generative neural networks
description 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
author2 Frederique Elise Oggier
author_facet Frederique Elise Oggier
Chan, Keefe
format Final Year Project
author Chan, Keefe
author_sort Chan, Keefe
title Predictive and generative neural networks
title_short Predictive and generative neural networks
title_full Predictive and generative neural networks
title_fullStr Predictive and generative neural networks
title_full_unstemmed Predictive and generative neural networks
title_sort predictive and generative neural networks
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148514
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