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

11.06.2018

Englisch

9781484236451

APRESS

Nein

Timothy Masters

258

Broschiert

Autoencoding in the Complex Domain

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).

C++, CUDA C, AI, artificial intel, machine learning, deep learning, programming, algorithms, numerical, computer vision, CV, source code, belief networks, domain, complex domain, deep belief, networks, autoencoding

UYQ - Künstliche Intelligenz UMC - Compiler und Übersetzer UN - Datenbanken

254 mm

17.8 cm