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Books on the topic 'RNN (recurrent neural networks)'

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Published: 5 June 2025

Last updated: 16 July 2025

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1

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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2

Tyagi, Amit Kumar, and Ajith Abraham. Recurrent Neural Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822.

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3

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

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4

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

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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

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

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7

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

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8

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

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9

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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10

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

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11

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

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12

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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13

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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14

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

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15

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

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16

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

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17

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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18

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

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19

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

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20

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Abstract:

Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

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21

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

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22

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

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23

Mandic, Danilo, and Jonathan Chambers. Recurrent Neural Networks for Prediction. Wiley & Sons, Incorporated, John, 2003.

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24

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

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25

Hammer, Barbara. Learning with Recurrent Neural Networks. Springer London, Limited, 2007.

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26

Abraham, Ajith, and Amit Kumar Tyagi. Recurrent Neural Networks: Concepts and Applications. Taylor & Francis Group, 2022.

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27

Abraham, Ajith, and Amit Kumar Tyagi. Recurrent Neural Networks: Concepts and Applications. Taylor & Francis Group, 2022.

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28

Abraham, Ajith, and Tyagi Amit Kumar. Recurrent Neural Networks: Concepts and Applications. CRC Press LLC, 2022.

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29

Abraham, Ajith, and Tyagi Amit Kumar. Recurrent Neural Networks: Concepts and Applications. CRC Press LLC, 2022.

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30

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

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31

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

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32

Jain, Lakhmi C., and Larry Medsker. Recurrent Neural Networks: Design and Applications. Taylor & Francis Group, 1999.

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33

Abraham, Ajith, and Amit Kumar Tyagi. Recurrent Neural Networks: Concepts and Applications. Taylor & Francis Group, 2022.

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34

Jain, Lakhmi C., and Larry Medsker. Recurrent Neural Networks: Design and Applications. Taylor & Francis Group, 1999.

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35

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

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36

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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37

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

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38

Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin / Heidelberg, 2014.

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39

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

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40

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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41

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

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42

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

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43

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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44

(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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45

The Rnn/IEEE Symposium on Neuroinformatics and Neurocomputers: Rostov-On-Don, Russia, October 7-10,1992. Ieee, 1993.

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46

Michel, Anthony, and Derong Liu. Qualitative Analysis and Synthesis of Recurrent Neural Networks. Taylor & Francis Group, 2001.

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47

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

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48

Qualitative Analysis and Synthesis of Recurrent Neural Networks. CRC Press LLC, 2002.

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49

Recurrent Neural Networks: From Simple to Gated Architectures. Springer International Publishing AG, 2023.

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50

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

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