001433163 000__ 04477cam\a2200685\i\4500
001433163 001__ 1433163
001433163 003__ OCoLC
001433163 005__ 20230309003552.0
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001433163 008__ 201204s2021\\\\sz\a\\\\ob\\\\001\0\eng\d
001433163 019__ $$a1228876390$$a1232277988$$a1238201282$$a1246358635$$a1253405884
001433163 020__ $$a3030610810$$q(electronic book)
001433163 020__ $$a9783030610821$$q(print)
001433163 020__ $$a3030610829
001433163 020__ $$a9783030610838$$q(print)
001433163 020__ $$a3030610837
001433163 020__ $$a9783030610814$$q(electronic bk.)
001433163 020__ $$z3030610802
001433163 020__ $$z9783030610807
001433163 0247_ $$a10.1007/978-3-030-61081-4$$2doi
001433163 035__ $$aSP(OCoLC)1228649876
001433163 040__ $$aSFB$$beng$$erda$$epn$$cSFB$$dOCLCO$$dOCLCF$$dGW5XE$$dEBLCP$$dYDX$$dOCLCO$$dGZM$$dDCT$$dVT2$$dLIP$$dOCLCQ$$dOCLCO$$dCOM$$dN$T$$dOCLCQ
001433163 049__ $$aISEA
001433163 050_4 $$aQ325.5
001433163 08204 $$a006.3/1$$223
001433163 1001_ $$aYan, Wei Qi,$$eauthor.
001433163 24510 $$aComputational methods for deep learning :$$btheoretic, practice and applications /$$cWei Qi Yan.
001433163 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001433163 300__ $$a1 online resource (xvii, 134 pages) :$$billustrations (some color)
001433163 336__ $$atext$$btxt$$2rdacontent
001433163 337__ $$acomputer$$bc$$2rdamedia
001433163 338__ $$aonline resource$$bcr$$2rdacarrier
001433163 347__ $$atext file
001433163 347__ $$bPDF
001433163 4901_ $$aTexts in computer science,$$x1868-0941
001433163 504__ $$aIncludes bibliographical references and index.
001433163 5050_ $$a1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning.
001433163 506__ $$aAccess limited to authorized users.
001433163 520__ $$aIntegrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.
001433163 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 15, 2021).
001433163 650_0 $$aMachine learning.
001433163 650_0 $$aNeural networks (Computer science)
001433163 650_0 $$aData mining.
001433163 650_0 $$aBig data.
001433163 650_0 $$aComputer science$$xMathematics.
001433163 650_6 $$aApprentissage automatique.
001433163 650_6 $$aRéseaux neuronaux (Informatique)
001433163 650_6 $$aExploration de données (Informatique)
001433163 650_6 $$aDonnées volumineuses.
001433163 650_6 $$aInformatique$$xMathématiques.
001433163 655_0 $$aElectronic books.
001433163 77608 $$iPrint version:$$z9783030610807
001433163 830_0 $$aTexts in computer science,$$x1868-0941
001433163 852__ $$bebk
001433163 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-61081-4$$zOnline Access$$91397441.1
001433163 909CO $$ooai:library.usi.edu:1433163$$pGLOBAL_SET
001433163 980__ $$aBIB
001433163 980__ $$aEBOOK
001433163 982__ $$aEbook
001433163 983__ $$aOnline
001433163 994__ $$a92$$bISE