Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks

Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward NetworksDeep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks
By 作者: Timothy Masters
ISBN-10 书号: 1484235908
ISBN-13 书号: 9781484235904
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2018-06-20
Pages 页数: 219
Book Description to Finelybook sorting
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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