Libros sobre el tema "Recurrent Neural Network architecture"

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1

Dayhoff, Judith E. Neural network architectures: An introduction. New York, N.Y: Van Nostrand Reinhold, 1990.

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2

T, Leondes Cornelius, ed. Neural network systems, techniques, and applications. San Diego: Academic Press, 1998.

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3

C, Jain L. y Johnson R. P, eds. Automatic generation of neural network architecture using evolutionary computation. Singapore: World Scientific, 1997.

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4

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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5

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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6

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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7

Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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8

United States. National Aeronautics and Space Administration., ed. A neural network architecture for implementation of expert sytems for real time monitoring. [Cincinnati, Ohio]: University of Cincinnati, College of Engineering, 1991.

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9

Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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10

United States. National Aeronautics and Space Administration., ed. A novel approach to noise-filtering based on a gain-scheduling neural network architecture. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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11

Lim, Chee Peng. A Multiple neural network architecture for sequential evidence aggregation and incomplete data classification. Sheffield: Univeristy of Sheffield, Dept. of Automatic Control and Systems Engineering, 1997.

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12

Salem, Fathi M. Recurrent Neural Networks: From Simple to Gated Architectures. Springer International Publishing AG, 2021.

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13

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

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14

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

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15

Mandic, Danilo P. y Jonathon A. Chambers. Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley & Sons, Incorporated, John, 2002.

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16

Magic, John y Mark Magic. Action Recognition Using Python and Recurrent Neural Network. Independently Published, 2019.

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17

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

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18

SpiNNaker: A Spiking Neural Network Architecture. now publishers, Inc., 2020. http://dx.doi.org/10.1561/9781680836523.

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19

SpiNNaker - a Spiking Neural Network Architecture. Now Publishers, 2020.

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20

Neural Network Architectures: An Introduction. Van Nostrand Reinhold, 1989.

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21

Magic, John y Mark Magic. Action Recognition: Step-By-step Recognizing Actions with Python and Recurrent Neural Network. Independently Published, 2019.

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22

Shan, Yunting, John Magic y Mark Magic. Action Recognition: Step-By-step Recognizing Actions with Python and Recurrent Neural Network. Independently Published, 2019.

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23

Hinton, Geoffrey E. Neural network architectures for artificial intelligence (Tutorial). American Association for Artificial Intelligence, 1988.

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24

Chiang, Chin. The architecture and design of a neural network classifier. 1990.

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25

Ho, Ki-Cheong. Optimisation of neural network architecture for modelling and control. 1998.

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26

Kane, Andrew J. An instruction systolic array architecture for multiple neural network types. 1998.

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27

A novel approach to noise-filtering based on a gain-scheduling neural network architecture. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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28

Parallel Implementation of an Artificial Neural Network Integrated Feature and Architecture Selection Algorithm. Storming Media, 1998.

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29

Mitchell, Laura, Vishnu Subramanian y Sri Yogesh K. Deep Learning with Pytorch 1. x: Implement Deep Learning Techniques and Neural Network Architecture Variants Using Python, 2nd Edition. Packt Publishing, Limited, 2019.

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30

Fletcher, Justin Barrows Swore. A constructive approach to hybrid architectures for machine learning. 1994.

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31

Thagard, Paul. Brain-Mind. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190678715.001.0001.

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Minds enable people to perceive, imagine, solve problems, understand, learn, speak, reason, create, and be emotional and conscious. Competing explanations of how the mind works have identified it as soul, computer, brain, dynamical system, or social construction. This book explains minds in terms of interacting mechanisms operating at multiple levels, including the social, mental, neural, and molecular. Brain–Mind presents a unified, brain-based theory of cognition and emotion with applications to the most complex kinds of thinking, right up to consciousness and creativity. Unification comes from systematic application of Chris Eliasmith’s powerful new Semantic Pointer Architecture, a highly original synthesis of neural network and symbolic ideas about how the mind works. The book shows the relevance of semantic pointers to a full range of important kinds of mental representations, from sensations and imagery to concepts, rules, analogies, and emotions. Neural mechanisms are used to explain many phenomena concerning consciousness, action, intention, language, creativity, and the self. This book belongs to a trio that includes Mind–Society: From Brains to Social Sciences and Professions and Natural Philosophy: From Social Brains to Knowledge, Reality, Morality, and Beauty. They can be read independently, but together they make up a Treatise on Mind and Society that provides a unified and comprehensive treatment of the cognitive sciences, social sciences, professions, and humanities.
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