Deep Belief Nets in C++ and CUDA C: Volume 1

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Beschreibung

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, yoüll 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 theroutines 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.

All theroutines 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.

Über den Autor

Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995)Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995) and Assessing and Improving Prediction and Classification (Apress, 2018).

Zusammenfassung

Master deep learning with C++ and CUDA C

Utilize restricted Boltzmann machines

Work with supervised feedforward networks

Inhaltsverzeichnis

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

Details

Erscheinungsjahr:	2018
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	ix 219 S. 13 s/w Illustr. 20 farbige Illustr. 219 p. 33 illus. 20 illus. in color.
ISBN-13:	9781484235904
ISBN-10:	1484235908
Sprache:	Englisch
Herstellernummer:	978-1-4842-3590-4
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Masters, Timothy
Auflage:	1st ed.
Hersteller:	Apress Apress L.P.
Maße:	254 x 178 x 13 mm
Von/Mit:	Timothy Masters
Erscheinungsdatum:	24.04.2018
Gewicht:	0,445 kg

Artikel-ID: 111350369