Overview
- Master deep learning with C++ and CUDA C
- Utilize restricted Boltzmann machines
- Work with supervised feedforward networks
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (5 chapters)
Keywords
About this book
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 theroutines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines.
What You Will Learn
- 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
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 1
Book Subtitle: Restricted Boltzmann Machines and Supervised Feedforward Networks
Authors: Timothy Masters
DOI: https://doi.org/10.1007/978-1-4842-3591-1
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: 978-1-4842-3590-4Published: 24 April 2018
eBook ISBN: 978-1-4842-3591-1Published: 23 April 2018
Edition Number: 1
Number of Pages: IX, 219
Number of Illustrations: 13 b/w illustrations, 20 illustrations in colour
Topics: Artificial Intelligence, Programming Languages, Compilers, Interpreters, Big Data, Big Data/Analytics