01133nam0 22002891i 450 UON0015306420231205102930.46420020107d1920 |0itac50 bajpnJP|||| 1||||Shunshoku umeogoyomiTamenaga ShunsuiTokyoTobunkan1920300 p.15 cmLETTERATURA GIAPPONESEPERIODO TOKUGAWA o EDO (1603-1867)TESTIUONC032106FIJPTōkyōUONL000031GIA VI AAGIAPPONE - LETTERATURA CLASSICA - FINO EDO - TESTIATamenaga ShunsuiUONV0132010TobunkanUONV264738650SADATAKA SasakiTamenaga ShunsuiUONV013203CHOJIRO EchizenyaTamenaga ShunsuiUONV013204ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00153064SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI GIA VI AA 455 N SI SA 102033 7 455 N Shunshoku umeogoyomi1273493UNIOR03621nam 22007455 450 991103485790332120251020130416.03-032-00910-310.1007/978-3-032-00910-4(MiAaPQ)EBC32364757(Au-PeEL)EBL32364757(CKB)41689376100041(DE-He213)978-3-032-00910-4(EXLCZ)994168937610004120251020d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDifference Equations and Machine Learning /by Dušan Stipanović1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (187 pages)Synthesis Lectures on Mathematics & Statistics,1938-17513-032-00909-X Introduction -- Linear Difference Equations -- Nonlinear Difference Equations -- Stability and Chaotic Behaviors of Difference Equations -- Control of Difference Equations -- Applications to Neural Networks and Machine Learning -- Conclusions.This book presents in-depth explanations of well-known and recognized behaviors of neural networks in machine learning. In addition, the author provides novel technical analyses of behaviors of discrete-time dynamical systems modeled as difference equations. These analyses and their outcomes are closely related to models of very well-known neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, which are widely used in machine learning and artificial intelligence (AI) applications. The author also discusses difference equations and their relevance to neural networks, machine learning, and AI. In addition, this book: Includes characterizations of difference equations and technical prospectives of discrete-time systems Provides new insights into the dynamical behaviors of some of the most popular neural networks used in machine learning Discusses novel technical analyses of discrete-time dynamical systems modeled as difference equations.Synthesis Lectures on Mathematics & Statistics,1938-1751Difference equationsFunctional equationsMachine learningArtificial intelligenceMathematical analysisNeural networks (Computer science)MathematicsDifference and Functional EquationsMachine LearningArtificial IntelligenceAnalysisMathematical Models of Cognitive Processes and Neural NetworksApplications of MathematicsDifference equations.Functional equations.Machine learning.Artificial intelligence.Mathematical analysis.Neural networks (Computer science)Mathematics.Difference and Functional Equations.Machine Learning.Artificial Intelligence.Analysis.Mathematical Models of Cognitive Processes and Neural Networks.Applications of Mathematics.515.625515.75Stipanović Dusan1852739MiAaPQMiAaPQMiAaPQBOOK9911034857903321Difference Equations and Machine Learning4448627UNINA