Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/474
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dc.contributor.authorMasters, Timothy. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:20Z-
dc.date.available2020-05-17T08:17:20Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235911 ;en_US
dc.identifier.isbn9781484235904 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/474-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9781484235904. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractDiscover the essential building blocks of the most common forms of deep belief networks. At each step this book provides 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. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 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. As such, you??ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. You will: Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important. ;en_US
dc.description.statementofresponsibilityby Timothy Masters.en_US
dc.description.tableofcontents1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. ;en_US
dc.format.extentIX, 219 p. 33 illus., 20 illus. in color. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235911.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectBig data. ;en_US
dc.subjectProgramming Languages and Electronic Computersen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectProgramming Languages and Compilers and Interpretersen_US
dc.subjectBig Data. ;en_US
dc.subjectBig Data/Analytics. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titleDeep Belief Nets in C++ and CUDA C: Volume 1en_US
dc.title.alternativeRestricted Boltzmann Machines and Supervised Feedforward Networks /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorMasters, Timothy. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:20Z-
dc.date.available2020-05-17T08:17:20Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235911 ;en_US
dc.identifier.isbn9781484235904 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/474-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9781484235904. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractDiscover the essential building blocks of the most common forms of deep belief networks. At each step this book provides 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. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 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. As such, you??ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. You will: Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important. ;en_US
dc.description.statementofresponsibilityby Timothy Masters.en_US
dc.description.tableofcontents1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. ;en_US
dc.format.extentIX, 219 p. 33 illus., 20 illus. in color. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235911.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectBig data. ;en_US
dc.subjectProgramming Languages and Electronic Computersen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectProgramming Languages and Compilers and Interpretersen_US
dc.subjectBig Data. ;en_US
dc.subjectBig Data/Analytics. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titleDeep Belief Nets in C++ and CUDA C: Volume 1en_US
dc.title.alternativeRestricted Boltzmann Machines and Supervised Feedforward Networks /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484235911.pdf4.03 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMasters, Timothy. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:20Z-
dc.date.available2020-05-17T08:17:20Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235911 ;en_US
dc.identifier.isbn9781484235904 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/474-
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9781484235904. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractDiscover the essential building blocks of the most common forms of deep belief networks. At each step this book provides 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. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 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. As such, you??ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. You will: Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important. ;en_US
dc.description.statementofresponsibilityby Timothy Masters.en_US
dc.description.tableofcontents1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. ;en_US
dc.format.extentIX, 219 p. 33 illus., 20 illus. in color. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235911.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectBig data. ;en_US
dc.subjectProgramming Languages and Electronic Computersen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectProgramming Languages and Compilers and Interpretersen_US
dc.subjectBig Data. ;en_US
dc.subjectBig Data/Analytics. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titleDeep Belief Nets in C++ and CUDA C: Volume 1en_US
dc.title.alternativeRestricted Boltzmann Machines and Supervised Feedforward Networks /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484235911.pdf4.03 MBAdobe PDFThumbnail
Preview File