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Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

Danilo Mandic, Jonathon Chambers




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 | #2031410 in Books |  2001-08-07 | Original language:English | PDF # 1 |  9.72 x.90 x6.83l,1.24 | File type: PDF | 304 pages

||10 of 13 people found the following review helpful.| Unexpected insights that make you go: "Aha!"|By Temujin Gautama|"Recurrent Neural Networks for Prediction: Learning Algorithms,
Architectures and Stability," approaches the field of recurrent neural networks from both a practical and a theoretical perspective. Starting from the fundamentals, where unexpected insights are offered even at the level of the dynamical richne|From the Back Cover|New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ca

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.
You can specify the type of files you want, for your device.Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability   |  Danilo Mandic, Jonathon Chambers.Not only was the story interesting, engaging and relatable, it also teaches lessons.

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