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Artificial Neural Networks in Public Policy: Towards an Analytical Framework

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dc.contributor.author Lee, Joshua
dc.date.accessioned 2020-04-27T20:40:09Z
dc.date.available 2020-04-27T20:40:09Z
dc.date.issued 2020-04
dc.identifier.uri http://hdl.handle.net/1920/11699
dc.description.abstract Interviews created during the course of research for the dissertation "Artificial Neural Networks in Public Policy: Towards an Analytical Framework." en_US
dc.language.iso en_US en_US
dc.relation.ispartofseries GMU School of Public Policy Working Papers;
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject neural network en_US
dc.subject deep learning en_US
dc.subject machine learning en_US
dc.title Artificial Neural Networks in Public Policy: Towards an Analytical Framework en_US
dc.title.alternative Artificial Neural Networks in Public Policy (Interview Audio Recordings) en_US
dc.type Sound en_US
dc.description.embargo 2100-01-01


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