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020 _a9783031168680
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082 0 4 _a006.3
_223
100 1 _aSun, Yanan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEvolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances
_h[electronic resource] /
_cby Yanan Sun, Gary G. Yen, Mengjie Zhang.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXVI, 331 p. 91 illus., 77 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1070
500 _aAcceso multiusuario
505 0 _aPart I: Fundamentals and Backgrounds -- Evolutionary Computation -- Deep Neural Networks -- Part II: Evolutionary Deep Neural Architecture Search for Unsupervised DNNs -- Architecture Design for Stacked AEs and DBNs -- Architecture Design for Convolutional Auto-Encoders -- Architecture Design for Variational Auto-Encoders -- Part III: Evolutionary Deep Neural Architecture Search for Supervised DNNs -- Architecture Design for Plain CNNs -- Architecture Design for RBs and DBs Based CNNs -- Architecture Design for Skip-Connection Based CNNs -- Hybrid GA and PSO for Architecture Design -- Internet Protocol Based Architecture Design -- Differential Evolution for Architecture Design -- Architecture Design for Analyzing Hyperspectral Images -- Part IV: Recent Advances in Evolutionary Deep Neural Architecture Search -- Encoding Space Based on Directed Acyclic Graphs -- End-to-End Performance Predictors -- Deep Neural Architecture Pruning -- Deep Neural Architecture Compression -- Distribution Training Framework for Architecture Design.
520 _aThis book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 1 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
700 1 _aYen, Gary G.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhang, Mengjie.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031168673
776 0 8 _iPrinted edition:
_z9783031168697
776 0 8 _iPrinted edition:
_z9783031168703
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1070
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-16868-0
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c260633
_d260632