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Deep Belief Nets in C++ and CUDA C.....

Masters, Timothy.

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Deep Belief Nets in C++ and CUDA C.. Volume 1,. Restricted Boltzmann machines and supervised feedforward networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Belief Nets in C++ and CUDA C./ by Timothy Masters.
其他題名:	Restricted Boltzmann machines and supervised feedforward networks
作者:	Masters, Timothy.
出版者:	Berkeley, CA :Apress : : 2018.,
面頁冊數:	ix, 219 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-1-4842-3591-1
ISBN:	9781484235911

Deep Belief Nets in C++ and CUDA C.. Volume 1,. Restricted Boltzmann machines and supervised feedforward networks
Masters, Timothy.

Deep Belief Nets in C++ and CUDA C.Volume 1,Restricted Boltzmann machines and supervised feedforward networks[electronic resource] /Restricted Boltzmann machines and supervised feedforward networksby Timothy Masters. - Berkeley, CA :Apress :2018. - ix, 219 p. :ill., digital ;24 cm.

1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual.

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. You will: 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.

ISBN: 9781484235911

Standard No.: 10.1007/978-1-4842-3591-1doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .M368 2018

Dewey Class. No.: 006.32

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