Deep Belief Nets in C++ and CUDA C: Volume 2

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Beschreibung

Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. Yoüll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for preprocessing time series and image data. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, yoüll learn a method for embedding class information in the input layer of a restricted Boltzmann machine. This facilitates generative display of samples from individual classes rather than the entire data distribution. The ability to see the features that the model has learned for each class separately can be invaluable.

At each step this book provides you with intuitive motivation, a summary of the most important equations relevant to the topic, and highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards.

What You'll Learn

Code for deep learning, neural networks, and AI using C++ and CUDA C

Carry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and more

Use the Fourier Transform for image preprocessing

Implement autoencoding via activation in the complex domain

Work with algorithms for CUDA gradient computation

Use the DEEP operating manual

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.

What You'll Learn

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) Assessing and Improving Prediction and Classification (CreateSpace, 2013) Deep Belief Nets in C++ and CUDA C: Volume I: Restricted Boltzmann Machines and Supervised Feedforward Networks (CreateSpace, 2015).

Zusammenfassung

A practical book with source code and algorithms on deep learning with C++ and CUDA C

Second of three books in a series on C++ and CUDA C deep learning and belief nets

Author is an authority on numerical C++ and algorithms in practice

Inhaltsverzeichnis

0. Introduction.- 1. Embedded Class Labels.- 2. Signal Preprocessing.- 3. Image Preprocessing.- 4. Autoencoding.- 5. Deep Operating Manual.

Details

Erscheinungsjahr:	2018
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xi 258 S. 47 s/w Illustr. 258 p. 47 illus.
ISBN-13:	9781484236451
ISBN-10:	1484236459
Sprache:	Englisch
Herstellernummer:	978-1-4842-3645-1
Einband:	Kartoniert / Broschiert
Autor:	Masters, Timothy
Auflage:	First Edition
Hersteller:	APRESS
Verantwortliche Person für die EU:	APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com
Maße:	254 x 178 x 15 mm
Von/Mit:	Timothy Masters
Erscheinungsdatum:	11.06.2018
Gewicht:	0,518 kg

Artikel-ID: 113049170