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Record Nr. |
UNINA9910300747503321 |
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Autore |
Masters Timothy |
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Titolo |
Deep Belief Nets in C++ and CUDA C: Volume 1 : Restricted Boltzmann Machines and Supervised Feedforward Networks / / by Timothy Masters |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (225 pages) : illustrations |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Programming languages (Electronic computers) |
Big data |
Artificial Intelligence |
Programming Languages, Compilers, Interpreters |
Big Data |
Big Data/Analytics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual. |
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Sommario/riassunto |
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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 |
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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. |
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