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Books on the topic 'Recurrent neural networks BLSTM'

Author: Grafiati

Published: 25 May 2022

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1

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

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2

Salem, Fathi M. Recurrent Neural Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89929-5.

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3

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

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4

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

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5

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

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6

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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7

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

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8

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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9

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

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10

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

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11

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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12

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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13

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

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14

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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15

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

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16

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

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17

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

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18

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

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19

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

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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

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

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32

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

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33

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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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

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

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36

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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37

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

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38

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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39

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, increasingly specific patterns of synaptic activation converge in response to (or in anticipation of) recurrent emotions, creating synaptic networks that link multiple regions. These networks regulate emotions (in real time). But they also stabilize and consolidate with repetition, thus giving rise tohabitsthat are the hallmark of individual development. These configurations are progressively sculpted through individual learning experiences, but they also become increasingly effective with use, thereby expressing both individual trajectories and normative advances as they develop. In sum, experience-driven synaptic changes create a repertoire of individual solutions to universal challenges, shared among members of a culture or society. This description casts individual differences and age-related advances as dual facets of a unitary developmental process.

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40

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 some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

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