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Review : Deep learning in electron microscopy
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Ede, Jeffrey M. (2020) Review : Deep learning in electron microscopy. Working Paper. Cornell University: arXiv. (Unpublished)
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Official URL: https://arxiv.org/abs/2009.08328v2
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
| Item Type: | Working or Discussion Paper (Working Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics |
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| Divisions: | Faculty of Science > Physics | |||||||||
| Library of Congress Subject Headings (LCSH): | Machine learning, Electron microscopy , Electron microscopy -- Data processing | |||||||||
| Publisher: | arXiv | |||||||||
| Place of Publication: | Cornell University | |||||||||
| Official Date: | 18 September 2020 | |||||||||
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| Number of Pages: | 97 | |||||||||
| Institution: | University of Warwick | |||||||||
| Status: | Not Peer Reviewed | |||||||||
| Publication Status: | Unpublished | |||||||||
| Access rights to Published version: | Open Access | |||||||||
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