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010   $a1-4842-3721-8
024 7 $a10.1007/978-1-4842-3721-2
035   $a(CKB)3850000000033398
035   $a(MiAaPQ)EBC5448082
035   $a(DE-He213)978-1-4842-3721-2
035   $a(CaSebORM)9781484237212
035   $a(PPN)22950616X
035   $a(OCoLC)1048260531
035   $a(OCoLC)on1048260531
035   $a(EXLCZ)993850000000033398
100   $a20180704d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDeep Belief Nets in C++ and CUDA C: Volume 3 $eConvolutional Nets /$fby Timothy Masters
205   $a1st ed. 2018.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018.
215   $a1 online resource (184 pages)
311   $a1-4842-3720-X 
327   $a1. Feedforward Networks -- 2. Programming Algorithms -- 3. CUDA Code -- 4. CONVNET Manual.
330   $aDiscover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ?thought process? that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download. You will: Discover convolutional nets and how to use them Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs Master the various programming algorithms required Carry out multi-threaded gradient computations and memory allocations for this threading Work with CUDA code implementations of all core computations, including layer activations and gradient calculations Make use of the CONVNET program and manual to explore convolutional nets and case studies.
517 3 $aConvolutional nets
517 3 $aDeep belief nets in C plus plus and CUDA C
606   $aArtificial intelligence
606   $aProgramming languages (Electronic computers)
606   $aBig data
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070
615  0$aArtificial intelligence.
615  0$aProgramming languages (Electronic computers).
615  0$aBig data.
615 14$aArtificial Intelligence.
615 24$aProgramming Languages, Compilers, Interpreters.
615 24$aBig Data.
615 24$aBig Data/Analytics.
676   $a006.32
700   $aMasters$b Timothy$4aut$4http://id.loc.gov/vocabulary/relators/aut$0105163
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910300757003321
996   $aDeep Belief Nets in C++ and CUDA C: Volume 3$92514104
997   $aUNINA