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From interest rates to software stocks, here's what Wall Street's top technical experts are watching. After strong back-to-back years for the stock market, investors are fixated on whether the ...
New Concepts in Technical Trading Systems. Trend Research, 1978. ISBN 0-89459-027-8; Ladis Konecny, Stocks and Exchange – the only Book you need, 2013, ISBN 9783848220656, technical analysis = chapter 8. Schabackers, Richard W. Stock Market Theory and Practice, 2011. ISBN 9781258159474
These networks are commonly referred to as backpropagation networks. Another form of ANN that is more appropriate for stock prediction is the time recurrent neural network (RNN) or time delay neural network (TDNN). Examples of RNN and TDNN are the Elman, Jordan, and Elman-Jordan networks.
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Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0 ...
Indexes closed lower in the first trading day of the year on Thursday. The losses extend the market's losing streak to five days, putting the Santa Claus rally at risk. Apple stock dropped more ...
With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
SNNS research neural network simulator. Historically, the most common type of neural network software was intended for researching neural network structures and algorithms. The primary purpose of this type of software is, through simulation, to gain a better understanding of the behavior and the properties of neural network
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