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Applications of deep convolutional neural networks to digitized natural history collections.

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Bibliographic Details
Title:	Applications of deep convolutional neural networks to digitized natural history collections.
Authors:	Schuettpelz E; National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America., Frandsen PB; Office of the Chief Information Officer, Smithsonian Institution, Washington, DC, United States of America., Dikow RB; Office of the Chief Information Officer, Smithsonian Institution, Washington, DC, United States of America., Brown A; NVIDIA, Santa Clara, CA, United States of America., Orli S; National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America., Peters M; National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America., Metallo A; Office of the Chief Information Officer, Smithsonian Institution, Washington, DC, United States of America., Funk VA; National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America., Dorr LJ; National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America.
Source:	Biodiversity data journal [Biodivers Data J] 2017 Nov 02 (5), pp. e21139. Date of Electronic Publication: 2017 Nov 02 (Print Publication: 2017).
Publication Type:	Journal Article
Journal Info:	Publisher: Pensoft Publishers Country of Publication: Bulgaria NLM ID: 101619899 Publication Model: eCollection Cited Medium: Print ISSN: 1314-2828 (Print) Linking ISSN: 13142828 NLM ISO Abbreviation: Biodivers Data J Subsets: PubMed not MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1314-2828
DOI:	10.3897/BDJ.5.e21139