Books on the topic 'Recurrent Neural Network architecture'

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

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

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

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

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

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

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

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.

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.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mitchell, Laura, Vishnu Subramanian, and 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.

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

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