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Deep Belief Nets in C++ and CUDA C: Volume 3 - Convolutional Nets (Paperback, 1st ed.)
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Deep Belief Nets in C++ and CUDA C: Volume 3 - Convolutional Nets (Paperback, 1st ed.)
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Discover the essential building blocks of a common and powerful
form of deep belief network: convolutional nets. This book 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. These models are especially useful
for image processing applications. 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.
General
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