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

Autoencoding in the Complex Domain

Timothy Masters (Autor)

Buch | Softcover

258 Seiten

2018 | 1st ed.
Apress (Verlag)
978-1-4842-3645-1 (ISBN)

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Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll 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, you’ll 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.

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).

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

Erscheinungsdatum	21.06.2018
Zusatzinfo	47 Illustrations, black and white; XI, 258 p. 47 illus.
Verlagsort	Berkley
Sprache	englisch
Maße	178 x 254 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Mathematik / Informatik ► Informatik ► Software Entwicklung
	Informatik ► Theorie / Studium ► Compilerbau
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Wirtschaft ► Allgemeines / Lexika
Schlagworte	AI • algorithms • artificial intel • autoencoding • Belief Networks • C++ • complex domain • computer vision • CUDA C • CV • deep belief • Deep learning • Domain • machine learning • Networks • Numerical • programming • source code
ISBN-10	1-4842-3645-9 / 1484236459
ISBN-13	978-1-4842-3645-1 / 9781484236451
Zustand	Neuware