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000789893 019__ $$a990633329$$a991559176
000789893 020__ $$a9781484228456$$q(electronic book)
000789893 020__ $$a1484228456$$q(electronic book)
000789893 020__ $$z9781484228449
000789893 035__ $$aSP(OCoLC)ocn990267808
000789893 035__ $$aSP(OCoLC)990267808$$z(OCoLC)990633329$$z(OCoLC)991559176
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000789893 049__ $$aISEA
000789893 050_4 $$aTA345.5.M42
000789893 08204 $$a511/.8$$223
000789893 1001_ $$aKim, Phil,$$eauthor.
000789893 24510 $$aMATLAB deep learning :$$bwith machine learning, neural networks and artificial intelligence /$$cPhil Kim.
000789893 264_1 $$a[New York, NY] :$$bApress,$$c2017.
000789893 300__ $$a1 online resource.
000789893 336__ $$atext$$btxt$$2rdacontent
000789893 337__ $$acomputer$$bc$$2rdamedia
000789893 338__ $$aonline resource$$bcr$$2rdacarrier
000789893 500__ $$aIncludes index.
000789893 5050_ $$aAt a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learning?; Challenges with Machine Learning; Overfitting; Confronting Overfitting; Types of Machine Learning; Classification and Regression; Summary; Chapter 2: Neural Network; Nodes of a Neural Network; Layers of Neural Network; Supervised Learning of a Neural Network; Training of a Single-Layer Neural Network: Delta Rule; Generalized Delta Rule; SGD, Batch, and Mini Batch; Stochastic Gradient Descent; Batch; Mini Batch
000789893 5058_ $$aExample: Delta RuleImplementation of the SGD Method; Implementation of the Batch Method; Comparison of the SGD and the Batch; Limitations of Single-Layer Neural Networks; Summary; Chapter 3: Training of Multi-Layer Neural Network; Back-Propagation Algorithm; Example: Back-Propagation; XOR Problem; Momentum; Cost Function and Learning Rule; Example: Cross Entropy Function; Cross Entropy Function; Comparison of Cost Functions; Summary; Chapter 4: Neural Network and Classification; Binary Classification; Multiclass Classification; Example: Multiclass Classification; Summary
000789893 5058_ $$aChapter 5: Deep LearningImprovement of the Deep Neural Network; Vanishing Gradient; Overfitting; Computational Load; Example: ReLU and Dropout; ReLU Function; Dropout; Summary; Chapter 6: Convolutional Neural Network; Architecture of ConvNet; Convolution Layer; Pooling Layer; Example: MNIST; Summary; Index
000789893 506__ $$aAccess limited to authorized users.
000789893 588__ $$aVendor-supplied metadata.
000789893 63000 $$aMATLAB.
000789893 650_0 $$aMachine learning.
000789893 650_0 $$aNeural networks (Computer science)
000789893 852__ $$bebk
000789893 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-1-4842-2845-6$$zOnline Access$$91397441.1
000789893 909CO $$ooai:library.usi.edu:789893$$pGLOBAL_SET
000789893 980__ $$aEBOOK
000789893 980__ $$aBIB
000789893 982__ $$aEbook
000789893 983__ $$aOnline
000789893 994__ $$a92$$bISE