01351nam0 22003853i 450 MIL020000520251003044228.00070542368International edition007100944220241218d1991 ||||0itac50 baengusz01i xxxe z01nz01ncRDAcarrierFunctional analysisWalter Rudin2. edNew York [etc.]McGraw-Hill©1991XV, 424 p.24 cmInternational series in pure and applied mathematicsBibliografia: P. 412-413.001SBL02374112001 International series in pure and applied mathematicsAnalisi funzionaleFIRCFIC010268E515ANALISI MATEMATICA14515.7ANALISI FUNZIONALE20515.7ANALISI FUNZIONALE22Rudin, WalterRAVV0725150701759ITIT-00000020241218IT-BN0095 NAP 01SALA DING $MIL0200005Biblioteca Centralizzata di Ateneo1 v. 01SALA DING 515 RUD.fu 0102 0000008805 VMA A4 1 v.Y 1994041819940418 01Functional analysis74569UNISANNIO03968nam 22006135 450 991062929610332120251113183355.09783031168680303116868210.1007/978-3-031-16868-0(MiAaPQ)EBC7133758(Au-PeEL)EBL7133758(CKB)25299467800041(OCoLC)1350687879(PPN)266349102(DE-He213)978-3-031-16868-0(EXLCZ)992529946780004120221108d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierEvolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances /by Yanan Sun, Gary G. Yen, Mengjie Zhang1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (335 pages)Studies in Computational Intelligence,1860-9503 ;10709783031168673 3031168674 Includes bibliographical references.Part 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.This 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.Studies in Computational Intelligence,1860-9503 ;1070Computational intelligenceArtificial intelligenceComputational IntelligenceArtificial IntelligenceComputational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.060006.3Sun Yanan1265805Yen Gary G.Zhang MengjieMiAaPQMiAaPQMiAaPQBOOK9910629296103321Evolutionary Deep Neural Architecture Search2968249UNINA