LEADER 04214nam  2200661   450 
001 9910741163303321
005 20220602111231.0
010   $a9783319944630$b(ebook)
010   $a3319944630$b(electronic book)
010   $a9783319944647$b(print)
010   $a3319944649
010   $a9783319944623$b(print)
010   $z3319944622
024 7 $a10.1007/978-3-319-94463-0
035   $a(CKB)4100000005958361
035   $a(DE-He213)978-3-319-94463-0
035   $a(MiAaPQ)EBC6237296
035   $a(PPN)229919332
035   $a(EXLCZ)994100000005958361
100   $a20180825d2018      u|             0
101 0 $aeng
135   $aurnn#---mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aNeural networks and deep learning $ea textbook /$fby Charu C. Aggarwal
205   $a1st ed.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XXIII, 497 pages 139 illustrations, 11 illustrations in color.)
311 1 $a3-319-94462-2 
320   $aIncludes bibliographic references and index.
327   $a1 An Introduction to Neural Networks -- 2 Machine Learning with Shallow Neural Networks -- 3 Training Deep Neural Networks -- 4 Teaching Deep Learners to Generalize -- 5 Radical Basis Function Networks -- 6 Restricted Boltzmann Machines -- 7 Recurrent Neural Networks -- 8 Convolutional Neural Networks -- 9 Deep Reinforcement Learning -- 10 Advanced Topics in Deep Learning.
330   $aThis book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
606   $aArtificial intelligence
606   $aComputers
606   $aMicroprocessors
606   $aMachine learning
606   $aNeural networks (Computer science)
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008
606   $aProcessor Architectures$3https://scigraph.springernature.com/ontologies/product-market-codes/I13014
615  0$aArtificial intelligence.
615  0$aComputers.
615  0$aMicroprocessors.
615  0$aMachine learning.
615  0$aNeural networks (Computer science)
615 14$aArtificial Intelligence.
615 24$aInformation Systems and Communication Service.
615 24$aProcessor Architectures.
676   $a006.32
700   $aAggarwal$b Charu C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910741163303321
996   $aNeural networks and deep learning$91904942
997   $aUNINA