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001480972 001__ 1480972
001480972 003__ OCoLC
001480972 005__ 20231031003317.0
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001480972 008__ 230920s2023\\\\si\a\\\\ob\\\\001\0\eng\d
001480972 019__ $$a1398279467
001480972 020__ $$a9789819948239$$q(electronic bk.)
001480972 020__ $$a9819948231$$q(electronic bk.)
001480972 020__ $$z9789819948222
001480972 020__ $$z9819948223
001480972 0247_ $$a10.1007/978-981-99-4823-9$$2doi
001480972 035__ $$aSP(OCoLC)1398313401
001480972 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCO
001480972 049__ $$aISEA
001480972 050_4 $$aQ325.5
001480972 08204 $$a006.3/1$$223/eng/20230920
001480972 1001_ $$aYan, Wei Qi,$$eauthor.
001480972 24510 $$aComputational methods for deep learning :$$btheory, algorithms, and implementations /$$cWei Qi Yan.
001480972 250__ $$aSecond edition.
001480972 264_1 $$aSingapore :$$bSpringer,$$c2023.
001480972 300__ $$a1 online resource (xx, 222 pages) :$$billustrations (some color).
001480972 336__ $$atext$$btxt$$2rdacontent
001480972 337__ $$acomputer$$bc$$2rdamedia
001480972 338__ $$aonline resource$$bcr$$2rdacarrier
001480972 4901_ $$aTexts in computer science,$$x1868-095X
001480972 504__ $$aIncludes bibliographical references and index.
001480972 5050_ $$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.
001480972 506__ $$aAccess limited to authorized users.
001480972 520__ $$aThe first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
001480972 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 20, 2023).
001480972 650_6 $$aApprentissage automatique.
001480972 650_6 $$aRéseaux neuronaux (Informatique)
001480972 650_6 $$aExploration de données (Informatique)
001480972 650_6 $$aDonnées volumineuses.
001480972 650_6 $$aInformatique$$xMathématiques.
001480972 650_0 $$aMachine learning.$$vCongresses$$0(DLC)sh2008107143
001480972 650_0 $$aNeural networks (Computer science)$$vCongresses$$0(DLC)sh2008108385
001480972 650_0 $$aData mining.$$vCongresses$$0(DLC)sh2008102035
001480972 650_0 $$aBig data.$$0(DLC)sh2012003227
001480972 650_0 $$aComputer science$$xMathematics.$$0(DLC)sh2007006411
001480972 655_0 $$aElectronic books.
001480972 77608 $$iPrint version: $$z9819948223$$z9789819948222$$w(OCoLC)1384414594
001480972 830_0 $$aTexts in computer science,$$x1868-095X
001480972 852__ $$bebk
001480972 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4823-9$$zOnline Access$$91397441.1
001480972 909CO $$ooai:library.usi.edu:1480972$$pGLOBAL_SET
001480972 980__ $$aBIB
001480972 980__ $$aEBOOK
001480972 982__ $$aEbook
001480972 983__ $$aOnline
001480972 994__ $$a92$$bISE