000			01434 a2200193 4500
020			_a9780939950812
082			_a006.31 _bD981m
100			_aDyar, Melinda Darby
245			_aMineralogy and optical mineralogy _cMelinda Darby Dyar and Mickey E. Gunter; illustrated by Dennis Tasa
260			_bMineralogical Society of America _c2008 _aChantilly
300			_axxiv, 708p
520			_aDeep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Deep learning is at the heart of artificial intelligence, and achievements and errors in the field are driving a great and constant interest.
650			_aDeep learning -- Machine learning
650			_aBig data -- Statistical methods
650			_aR [Computer program language]
700			_aGunter, Mickey E.
700			_aTasa, Dennis [ill.]
942			_cTXT
999			_c567478 _d567478