LEADER 03964nam  22006255  450 
001 9910300744803321
005 20200703082839.0
010   $a1-4842-3646-7
024 7 $a10.1007/978-1-4842-3646-8
035   $a(CKB)4100000004243398
035   $a(DE-He213)978-1-4842-3646-8
035   $a(MiAaPQ)EBC5406338
035   $a(CaSebORM)9781484236468
035   $a(PPN)227407016
035   $a(OCoLC)1041858436
035   $a(OCoLC)on1041858436
035   $a(EXLCZ)994100000004243398
100   $a20180529d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDeep Belief Nets in C++ and CUDA C: Volume 2 $eAutoencoding in the Complex Domain /$fby Timothy Masters
205   $a1st ed. 2018.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018.
215   $a1 online resource (XI, 258 p. 47 illus.) 
300   $aIncludes index.
311   $a1-4842-3645-9 
327   $a0. Introduction -- 1. Embedded Class Labels -- 2. Signal Preprocessing -- 3. Image Preprocessing -- 4. Autoencoding -- 5. Deep Operating Manual.
330   $aDiscover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You?ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for preprocessing time series and image data. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, you?ll learn a method for embedding class information in the input layer of a restricted Boltzmann machine. This facilitates generative display of samples from individual classes rather than the entire data distribution. The ability to see the features that the model has learned for each class separately can be invaluable. At each step this book provides you with intuitive motivation, a summary of the most important equations relevant to the topic, and highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. You will: ? Code for deep learning, neural networks, and AI using C++ and CUDA C ? Carry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and more ? Use the Fourier Transform for image preprocessing ? Implement autoencoding via activation in the complex domain ? Work with algorithms for CUDA gradient computation ? Use the DEEP operating manual.
517 3 $aAutoencoding in the complex domain
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
700   $aMasters$b Timothy$4aut$4http://id.loc.gov/vocabulary/relators/aut$0105163
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910300744803321
996   $aDeep Belief Nets in C++ and CUDA C: Volume 2$92544830
997   $aUNINA