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001469458 001__ 1469458
001469458 003__ OCoLC
001469458 005__ 20230803003331.0
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001469458 007__ cr\un\nnnunnun
001469458 008__ 230605s2023\\\\sz\\\\\\o\\\\\001\0\eng\d
001469458 019__ $$a1381712232
001469458 020__ $$a9783031295553$$q(electronic bk.)
001469458 020__ $$a3031295552$$q(electronic bk.)
001469458 020__ $$z3031295544
001469458 020__ $$z9783031295546
001469458 0247_ $$a10.1007/978-3-031-29555-3$$2doi
001469458 035__ $$aSP(OCoLC)1381107322
001469458 040__ $$aYDX$$beng$$erda$$cYDX$$dEBLCP$$dGW5XE$$dUKAHL$$dOCLCF$$dOH1
001469458 049__ $$aISEA
001469458 050_4 $$aQA76.87
001469458 08204 $$a006.32
001469458 1001_ $$aSen, Zekai,$$eauthor.
001469458 24510 $$aShallow and deep learning principles :$$bscientific, philosophical, and logical perspectives /$$cZekai Sen
001469458 264_1 $$aCham :$$bSpringer,$$c[2023]
001469458 300__ $$a1 online resource
001469458 336__ $$atext$$btxt$$2rdacontent
001469458 337__ $$acomputer$$bc$$2rdamedia
001469458 338__ $$aonline resource$$bcr$$2rdacarrier
001469458 504__ $$aIncludes bibliographical references and index.
001469458 5050_ $$aIntroduction -- Philosophical and Logical Principles in Science -- Uncertainty and Modeling Principles -- Mathematical Modeling Principles -- Genetic Algorithm -- Artificial Neural Networks -- Artfcal Intellgence -- Machne Learnng -- Deep Learning -- Conclusion.
001469458 506__ $$aAccess limited to authorized users.
001469458 520__ $$aThis book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules.
001469458 650_0 $$aNeural networks (Computer science)
001469458 650_0 $$aMachine learning.
001469458 655_0 $$aElectronic books.
001469458 77608 $$iPrint version: $$z3031295544$$z9783031295546$$w(OCoLC)1371402151
001469458 852__ $$bebk
001469458 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-29555-3$$zOnline Access$$91397441.1
001469458 909CO $$ooai:library.usi.edu:1469458$$pGLOBAL_SET
001469458 980__ $$aBIB
001469458 980__ $$aEBOOK
001469458 982__ $$aEbook
001469458 983__ $$aOnline
001469458 994__ $$a92$$bISE