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Books on the topic 'CNN (Convolutional Neural Networks)'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

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

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2

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

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3

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

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4

Venkatesan, Ragav, and Baoxin Li. Convolutional Neural Networks in Visual Computing. CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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

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11

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

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12

Shi, Feng. Learn About Convolutional Neural Networks in Python With Data From the MNIST Dataset (1998). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497802.

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13

Abich, Geancarlo, Luciano Ost, and Ricardo Reis. Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-18599-1.

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14

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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15

Saidane, Zohra. Image and video text recognition using convolutional neural networks: Study of new CNNs architectures for binarization, segmentation and recognition of text images. LAP Lambert Academic Publishing, 2011.

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16

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

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17

Kong, Joonho, and Mahmood Azhar Qureshi. Accelerators for Convolutional Neural Networks. Wiley & Sons, Incorporated, John, 2023.

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18

Kong, Joonho, and Mahmood Azhar Qureshi. Accelerators for Convolutional Neural Networks. Wiley & Sons, Incorporated, John, 2023.

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19

Kong, Joonho, and Mahmood Azhar Qureshi. Accelerators for Convolutional Neural Networks. Wiley & Sons, Incorporated, John, 2023.

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20

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

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21

Convolutional Neural Networks for Medical Applications. Springer, 2023.

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22

CONVOLUTIONAL NEURAL NETWORKS Examples with MATLAB. Lulu Press, Inc., 2023.

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23

Duffner, Stefan. Face Image Analysis with Convolutional Neural Networks. GRIN Verlag GmbH, 2009.

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24

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

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25

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

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26

Shah, Syed Afaq Ali, Salman Khan, Mohammed Bennamoun, and Hossein Rahmani. Guide to Convolutional Neural Networks for Computer Vision. Springer International Publishing AG, 2018.

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27

Jin, Zhi, and Lili Mou. Tree-Based Convolutional Neural Networks: Principles and Applications. Springer, 2018.

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28

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

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29

Öztürk, Saban. Convolutional Neural Networks for Medical Image Processing Applications. CRC Press LLC, 2022.

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30

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

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31

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

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32

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

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33

Ozturk, Saban. Convolutional Neural Networks for Medical Image Processing Applications. CRC Press LLC, 2022.

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34

IoT-Enabled Convolutional Neural Networks: Techniques and Applications. River Publishers, 2023.

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35

IoT-Enabled Convolutional Neural Networks: Techniques and Applications. River Publishers, 2023.

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36

IoT-Enabled Convolutional Neural Networks: Techniques and Applications. River Publishers, 2023.

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37

Li, Baoxin, and Ragav Venkatesan. Convolutional Neural Networks in Visual Computing: A Concise Guide. Taylor & Francis Group, 2017.

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38

Li, Baoxin, and Ragav Venkatesan. Convolutional Neural Networks in Visual Computing: A Concise Guide. Taylor & Francis Group, 2017.

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39

A Guide to Convolutional Neural Networks for Computer Vision. Springer Nature, 2018.

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40

Convolutional Neural Networks in Visual Computing: A Concise Guide. Taylor & Francis Group, 2017.

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41

Li, Baoxin, and Ragav Venkatesan. Convolutional Neural Networks in Visual Computing: A Concise Guide. Taylor & Francis Group, 2017.

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42

Li, Baoxin, and Ragav Venkatesan. Convolutional Neural Networks in Visual Computing: A Concise Guide. Taylor & Francis Group, 2017.

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43

Li, Baoxin, and Ragav Venkatesan. Convolutional Neural Networks in Visual Computing: A Concise Guide. Taylor & Francis Group, 2017.

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44

Michelucci, Umberto. Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection. Apress, 2019.

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45

Practical Convolutional Neural Networks: Implement advanced deep learning models using Python. Packt Publishing - ebooks Account, 2018.

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46

Dimitoglou, George, and Ahmad Tafti. Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models. de Gruyter GmbH, Walter, 2024.

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47

Dimitoglou, George, and Ahmad Tafti. Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models. de Gruyter GmbH, Walter, 2024.

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48

Dimitoglou, George, and Ahmad Tafti. Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models. de Gruyter GmbH, Walter, 2024.

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49

Chen, G., Andrew Adamatzky, and Leon O. Chua. Chaos, CNN, Memristors and Beyond: A Festschrift for Leon Chua. World Scientific Publishing Co Pte Ltd, 2013.

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50

Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.

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