Overview
- Author is an authority on numerical C++ and algorithms in practice
- A practical book with source code and algorithms on deep learning with C++ and CUDA C
- Final third of three books in a series on C++ and CUDA C deep learning and belief nets
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Table of contents (4 chapters)
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Front Matter
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Back Matter
Keywords
About this book
At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download.
What You Will Learn
- Discover convolutional nets and how to use them
- Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
- Master the various programming algorithms required
- Carry out multi-threaded gradient computations and memory allocations for this threading
- Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
- Make use of the CONVNET program and manual to explore convolutional nets and case studies
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
Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C++ (Academic Press, 1993); Signal and Image Processing with Neural Networks (Wiley, 1994); Advanced Algorithms for Neural Networks (Wiley,1995); Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995); Data Mining Algorithms in C++ (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018); Deep Belief Nets in C++ and CUDA C: Volume 1 (Apress, 2018); and Deep Belief Nets in C++ and CUDA C: Volume 2 (Apress, 2018).
Bibliographic Information
Book Title: Deep Belief Nets in C++ and CUDA C: Volume 3
Book Subtitle: Convolutional Nets
Authors: Timothy Masters
DOI: https://doi.org/10.1007/978-1-4842-3721-2
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: 978-1-4842-3720-5Published: 05 July 2018
eBook ISBN: 978-1-4842-3721-2Published: 04 July 2018
Edition Number: 1
Number of Pages: XII, 176
Number of Illustrations: 13 b/w illustrations
Topics: Artificial Intelligence, Programming Languages, Compilers, Interpreters, Big Data, Big Data/Analytics