Deep belief nets in C++ and CUDA C : Volume 2, Autoencoding in the complex domain [electronic resource] / Timothy Masters.
Masters, Timothy.
2018
QA76.87
Available Online

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Title Deep belief nets in C++ and CUDA C : Volume 2, Autoencoding in the complex domain [electronic resource] / Timothy Masters.
Author Masters, Timothy.
ISBN 9781484236468 (electronic book)
1484236467 (electronic book)
9781484236451
1484236459
DOI https://doi.org/10.1007/978-1-4842-3646-8
Publication Details [Berkeley, CA] : Apress, [2018]
Language English
Description 1 online resource.
Item Number 10.1007/978-1-4842-3646-8 doi
Call Number QA76.87
Dewey Decimal Classification 006.32
Summary 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. 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.
Note Includes index.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (viewed June 7, 2018).
Available in Other Form Print version: 9781484236451
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0. Introduction
1. Embedded Class Labels
2. Signal Preprocessing
3. Image Preprocessing
4. Autoencoding
5. Deep Operating Manual.

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