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Books on the topic 'Recurent neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Hu, Xiaolin, and P. Balasubramaniam. Recurrent neural networks. InTech, 2008.

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2

Hammer, Barbara. Learning with recurrent neural networks. Springer London, 2000. http://dx.doi.org/10.1007/bfb0110016.

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3

ElHevnawi, Mahmoud, and Mohamed Mysara. Recurrent neural networks and soft computing. InTech, 2012.

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4

K, Tan K., ed. Convergence analysis of recurrent neural networks. Kluwer Academic Publishers, 2004.

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5

Yi, Zhang, and K. K. Tan. Convergence Analysis of Recurrent Neural Networks. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-3819-3.

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6

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012.

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7

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24797-2.

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8

Derong, Liu, ed. Qualitative analysis and synthesis of recurrent neural networks. Marcel Dekker, Inc., 2002.

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9

Chen, Wen. Recurrent neural networks applied to robotic motion control. National Library of Canada, 2002.

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10

Rovithakis, George A., and Manolis A. Christodoulou. Adaptive Control with Recurrent High-order Neural Networks. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0785-9.

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11

Bianchi, Filippo Maria, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, and Robert Jenssen. Recurrent Neural Networks for Short-Term Load Forecasting. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70338-1.

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12

Kuan, Chung-Ming. Forecasting exchange rates using feedforward and recurrent neural networks. University of Illinois at Urbana-Champaign, 1993.

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13

Kuan, Chung-Ming. Forecasting exchange rates using feedforward and recurrent neural networks. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1992.

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14

Rovithakis, George A. Adaptive control with recurrent high-order neural networks: Theory and industrial applications. Springer, 2000.

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15

Rovithakis, George A. Adaptive Control with Recurrent High-order Neural Networks: Theory and Industrial Applications. Springer London, 2000.

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16

Medsker, Larry, and Lakhmi C. Jain, eds. Recurrent Neural Networks. CRC Press, 1999. http://dx.doi.org/10.1201/9781420049176.

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17

Hu, Xiaolin, and P. Balasubramaniam, eds. Recurrent Neural Networks. InTech, 2008. http://dx.doi.org/10.5772/68.

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18

Hammer, Barbara. Learning with Recurrent Neural Networks. Springer, 2000.

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19

Yi, Zhang, and K. K. Tan. Convergence Analysis of Recurrent Neural Networks (Network Theory and Applications). Springer, 2003.

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20

Yi, Zhang. Convergence Analysis of Recurrent Neural Networks. Springer, 2013.

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21

R, Medsker L., and Jain L. C, eds. Recurrent neural networks: Design and applications. CRC Press, 2000.

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22

ElHefnawi, Mahmoud, ed. Recurrent Neural Networks and Soft Computing. InTech, 2012. http://dx.doi.org/10.5772/2296.

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23

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012.

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24

Supervised Sequence Labelling With Recurrent Neural Networks. Springer, 2012.

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25

Recurrent Neural Networks for Temporal Data Processing. InTech, 2011.

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26

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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27

Cardot, Hubert, ed. Recurrent Neural Networks for Temporal Data Processing. InTech, 2011. http://dx.doi.org/10.5772/631.

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28

1965-, Kolen John F., and Kremer Stefan C. 1968-, eds. A field guide to dynamical recurrent networks. IEEE Press, 2001.

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29

(Editor), John F. Kolen, and Stefan C. Kremer (Editor), eds. A Field Guide to Dynamical Recurrent Networks. Wiley-IEEE Press, 2001.

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30

Knowledge Acquisition and Representation in Recurrent Neural Networks. World Scientific Publishing Company, 2005.

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31

Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley, 2001.

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32

Mandic, Danilo P., and Jonathon A. Chambers. Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley & Sons, Incorporated, John, 2003.

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33

Michel, Anthony, and Derong Liu. Qualitative Analysis and Synthesis of Recurrent Neural Networks (Pure and Applied Mathematics). CRC, 2002.

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34

(Editor), Larry Medsker, and Lakhmi C. Jain (Editor), eds. Recurrent Neural Networks: Design and Applications (The Crc Press International Series on Computational Intelligence). CRC, 1999.

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35

Bianchi, Filippo Maria Maria, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, and Robert Jenssen. Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis. Springer, 2017.

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36

Kostadinov, Simeon. Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow. Packt Publishing, 2018.

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37

Gandhi, Vaibhav. Brain-Computer Interfacing for Assistive Robotics: Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces. Academic Press, 2014.

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38

Lewis, Marc D. The Development of Emotion Regulation. Edited by Philip David Zelazo. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199958474.013.0004.

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This chapter examines the relation between normative advances and emerging individual differences in emotion regulation (ER), using principles from developmental cognitive neuroscience to integrate these seemingly disparate processes. Like several other theorists, I view corticolimbic development as a self-organizing stream of synaptic alterations, driven by experience rather than biologically prespecified. This conceptualization helps resolve ambiguities that appear when we try, but consistently fail, to neatly parse individual differences and developmental differences. At the neural level, i

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39

Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes

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