LEADER 04067nam 22005655 450 001 9910337950003321 005 20200703005304.0 010 $a3-030-06073-X 024 7 $a10.1007/978-3-030-06073-2 035 $a(CKB)4100000007656558 035 $a(DE-He213)978-3-030-06073-2 035 $a(MiAaPQ)EBC5709961 035 $a(PPN)235005983 035 $a(EXLCZ)994100000007656558 100 $a20190215d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning: Fundamentals, Theory and Applications$b[electronic resource] /$fedited by Kaizhu Huang, Amir Hussain, Qiu-Feng Wang, Rui Zhang 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (VII, 163 p. 66 illus., 46 illus. in color.) 225 1 $aCognitive Computation Trends,$x2524-5341 ;$v2 311 $a3-030-06072-1 327 $aPreface -- Introduction to Deep Density Models with Latent Variables -- Deep RNN Architecture: Design and Evaluation -- Deep Learning Based Handwritten Chinese Character and Text Recognition -- Deep Learning and Its Applications to Natural Language Processing -- Deep Learning for Natural Language Processing -- Oceanic Data Analysis with Deep Learning Models -- Index. 330 $aThe purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications. 410 0$aCognitive Computation Trends,$x2524-5341 ;$v2 606 $aMedicine 606 $aArtificial intelligence 606 $aAlgorithms 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 615 0$aMedicine. 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 14$aBiomedicine, general. 615 24$aArtificial Intelligence. 615 24$aAlgorithms. 676 $a610 702 $aHuang$b Kaizhu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHussain$b Amir$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Qiu-Feng$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhang$b Rui$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910337950003321 996 $aDeep Learning: Fundamentals, Theory and Applications$92079139 997 $aUNINA