Overview
- A practical book with source code and algorithms on deep learning with C++ and CUDA C
- Second of three books in a series on C++ and CUDA C deep learning and belief nets
- Author is an authority on numerical C++ and algorithms in practice
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Table of contents (5 chapters)
Keywords
About this book
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 Learn
- 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
Who This Book Is For
Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Deep Belief Nets in C++ and CUDA C: Volume 2
Book Subtitle: Autoencoding in the Complex Domain
Authors: Timothy Masters
DOI: https://doi.org/10.1007/978-1-4842-3646-8
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Timothy Masters 2018
Softcover ISBN: 978-1-4842-3645-1Published: 11 June 2018
eBook ISBN: 978-1-4842-3646-8Published: 29 May 2018
Edition Number: 1
Number of Pages: XI, 258
Number of Illustrations: 47 b/w illustrations
Topics: Artificial Intelligence, Programming Languages, Compilers, Interpreters, Big Data, Big Data/Analytics