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Livros sobre o tema "Convolutional recurrent neural networks"

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Autor: Grafiati

Publicado: 1 de junho de 2024

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1

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

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2

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

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3

Hu, Xiaolin, e P. Balasubramaniam. Recurrent neural networks. Rijek, Crotia: InTech, 2008.

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4

Mou, Lili, e Zhi Jin. Tree-Based Convolutional Neural Networks. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1870-2.

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5

Milosevic, Nemanja. Introduction to Convolutional Neural Networks. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5648-0.

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6

Habibi Aghdam, Hamed, e Elnaz Jahani Heravi. Guide to Convolutional Neural Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57550-6.

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7

Venkatesan, Ragav, e Baoxin Li. Convolutional Neural Networks in Visual Computing. Boca Raton ; London : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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8

Teoh, Teik Toe. Convolutional Neural Networks for Medical Applications. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8814-1.

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9

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

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10

Koonce, Brett. Convolutional Neural Networks with Swift for Tensorflow. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2.

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11

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

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12

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

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13

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

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14

Yi, Zhang. Convergence analysis of recurrent neural networks. Boston: Kluwer Academic Publishers, 2004.

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15

Ozturk, Saban. Convolutional Neural Networks for Medical Image Processing Applications. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003215141.

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16

Naved, Mohd, V. Ajantha Devi, Loveleen Gaur e Ahmed A. Elngar. IoT-enabled Convolutional Neural Networks: Techniques and Applications. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003393030.

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17

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

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18

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

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19

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

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20

Khan, Salman, Hossein Rahmani, Syed Afaq Ali Shah e Mohammed Bennamoun. A Guide to Convolutional Neural Networks for Computer Vision. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01821-3.

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21

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

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22

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

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23

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

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24

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

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25

Lu, Le, Yefeng Zheng, Gustavo Carneiro e Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Image Computing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42999-1.

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26

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

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27

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

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28

Lu, Le, Xiaosong Wang, Gustavo Carneiro e Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13969-8.

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29

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

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30

Sangeetha, V., e 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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Resumo:

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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31

S, Ranjith M. Hunting Convolutional Neural Networks. Independently Published, 2019.

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32

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

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33

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

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34

Munir. Accelerators for Convolutional Neural Networks. Wiley & Sons, Limited, John, 2023.

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35

Convolutional Neural Networks for Medical Applications. Springer, 2023.

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36

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

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37

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

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38

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

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39

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

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40

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

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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

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49

Medioni, Gerard, Salman Khan, Mohammed Bennamoun, Hossein Rahmani e Syed Afaq Ali. Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers, 2018.

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50

Ozturk, Saban. Convolutional Neural Networks for Medical Image Processing Applications. Taylor & Francis Group, 2022.

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