9781484236451 - deep belief nets in c++ and cuda c: volume 2: autoencoding in the complex domain di masters, timothy (26 risultati)
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Paperback. Condizione: New. Discover 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 alg…orithms 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. What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
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Paperback. Condizione: New. Discover 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 alg…orithms 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. What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
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Paperback. Condizione: New. Discover 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 alg…orithms 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. What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
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Paperback. Condizione: New. Discover 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 alg…orithms 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. What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
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Taschenbuch. Condizione: Neu. Deep Belief Nets in C++ and CUDA C: Volume 2 | Autoencoding in the Complex Domain | Timothy Masters | Taschenbuch | xi | Englisch | 2018 | Apress | EAN 9781484236451 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hart…mann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Discover 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 application…s.Deep Belief Nets in C++ and CUDA C: Volume 2also 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 bookprovides 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.What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended. 272 pp. Englisch.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A practical book with source code and algorithms on deep learning with C++ and CUDA CSecond of three books in a series on C++ and CUDA C deep learning and belief netsAuthor is an authority on numerical.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Discover 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. D…eep 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch.
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Discover 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.Dee…p Belief Nets in C++ and CUDA C: Volume 2also 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 bookprovides 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.What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
