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Deep Belief Nets in C++ and CUDA C: Volume 1 - Restricted Boltzmann Machines and Supervised Feedforward Networks (Paperback, 1st ed.)
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Deep Belief Nets in C++ and CUDA C: Volume 1 - Restricted Boltzmann Machines and Supervised Feedforward Networks (Paperback, 1st ed.)
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Total price: R767
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Discover the essential building blocks of the most common forms of
deep belief networks. At each step this book provides 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. 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 the routines 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.
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