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Deep Belief Nets in C++ and CUDA C. Volume 1, Restricted Boltzmann machines and supervised feedforward networks /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Masters, Timothy, |e author. | |
| 245 | 1 | 0 | |a Deep Belief Nets in C++ and CUDA C. |n Volume 1, |p Restricted Boltzmann machines and supervised feedforward networks / |c Timothy Masters. |
| 246 | 3 | 0 | |a Restricted Boltzmann machines and supervised feedforward networks |
| 264 | 1 | |a New York, NY : |b Apress, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource (ix, 219 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a text file | ||
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| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed May 02, 2018). | |
| 505 | 0 | |a 1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. | |
| 520 | |a 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. | ||
| 542 | |f © Copyright 2018 Timothy Masters. |g 2018 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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| 700 | 1 | |a Patnayak, Chinmaya, |e technical reviewer. | |
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