9781484236451-1484236459-Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain

Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain

ISBN-13: 9781484236451
ISBN-10: 1484236459
Edition: 1st ed.
Author: Timothy Masters
Publication date: 2018
Publisher: Apress
Format: Paperback 269 pages
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Book details

ISBN-13: 9781484236451
ISBN-10: 1484236459
Edition: 1st ed.
Author: Timothy Masters
Publication date: 2018
Publisher: Apress
Format: Paperback 269 pages

Summary

Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain (ISBN-13: 9781484236451 and ISBN-10: 1484236459), written by authors Timothy Masters, was published by Apress in 2018. With an overall rating of 4.0 stars, it's a notable title among other books. You can easily purchase or rent Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain (Paperback) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

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 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. 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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