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Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain
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Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain

Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain

Timothy Masters

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258 pages, parution le 10/06/2018
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Résumé

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.0. Introduction1. Embedded Class Labels2. Signal Preprocessing3. Image Preprocessing4. Autoencoding5. Deep Operating ManualTimothy 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).

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Éditeur(s) Apress
Auteur(s) Timothy Masters
Parution 10/06/2018
Nb. de pages 258
EAN13 9781484236451

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