Deep learning: A guide for practitioners in the physical sciences
Author:
Affiliation:
1. Lawrence Livermore National Laboratory, Livermore, California 94551, USA
2. Texas A&M University, College Station, Texas 77843, USA
Publisher
AIP Publishing
Subject
Condensed Matter Physics
Link
http://aip.scitation.org/doi/pdf/10.1063/1.5020791
Reference22 articles.
1. Searching for exotic particles in high-energy physics with deep learning
2. A prediction tool for real-time application in the disruption protection system at JET
3. Opportunities and obstacles for deep learning in biology and medicine
4. Simultaneous diagnosis of radial profiles and mix in NIF ignition-scale implosions via X-ray spectroscopy
5. Approximation by superpositions of a sigmoidal function
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