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Books on the topic 'Deep neural networks architecture'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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1

Alsuhli, Ghada, Vasilis Sakellariou, Hani Saleh, Mahmoud Al-Qutayri, Baker Mohammad, and Thanos Stouraitis. Number Systems for Deep Neural Network Architectures. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-38133-1.

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2

Aggarwal, Charu C. Neural Networks and Deep Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94463-0.

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3

Aggarwal, Charu C. Neural Networks and Deep Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29642-0.

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4

Moolayil, Jojo. Learn Keras for Deep Neural Networks. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4240-7.

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5

1947-, Holden Arun V., and Kri͡ukov V. I. 1935-, eds. Neural networks: Theory and architecture. Manchester University Press, 1990.

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6

Caterini, Anthony L., and Dong Eui Chang. Deep Neural Networks in a Mathematical Framework. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1.

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7

Razaghi, Hooshmand Shokri. Statistical Machine Learning & Deep Neural Networks Applied to Neural Data Analysis. [publisher not identified], 2020.

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8

Fingscheidt, Tim, Hanno Gottschalk, and Sebastian Houben, eds. Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4.

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9

Modrzyk, Nicolas. Real-Time IoT Imaging with Deep Neural Networks. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5722-7.

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10

Iba, Hitoshi. Evolutionary Approach to Machine Learning and Deep Neural Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0200-8.

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11

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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12

Tetko, Igor V., Věra Kůrková, Pavel Karpov, and Fabian Theis, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3.

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13

Melcher, Kathrin, and Rosaria Silipo. Codeless Deep Learning with KNIME: Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. Packt Publishing, Limited, 2020.

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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

Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras. Packt Publishing, 2019.

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16

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.

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17

Transformers for Natural Language Processing: Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. Packt Publishing, Limited, 2021.

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18

Transformers for Natural Language Processing: Build, Train, and Fine-Tune Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, and GPT-3. Packt Publishing, Limited, 2022.

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19

Labonne, Maxime. Hands-On Graph Neural Networks Using Python: Practical Techniques and Architectures for Building Powerful Graph and Deep Learning Apps with Pytorch. Packt Publishing, Limited, 2023.

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20

Zhang, Yunong, Dechao Chen, and Chengxu Ye. Deep Neural Networks. Taylor & Francis Group, 2020.

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21

Graupe, Daniel. Deep Learning Neural Networks. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/10190.

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22

Nakamoto, Pat. Neural Networks and Deep Learning: Neural Networks & Deep Learning, Deep Learning, Blockchain Blueprint. Createspace Independent Publishing Platform, 2018.

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23

Stanimirovic, Ivan. Deep Neural Networks and Applications. Arcler Education Inc, 2019.

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24

Stanimirovic, Ivan. Deep Neural Networks and Applications. Arcler Education Inc, 2019.

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25

Davis, Ronald. Neural Networks and Deep Learning. Independently Published, 2017.

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26

Vidales, A. Deep Learning with Matlab: Neural Networks Design and Dynamic Neural Networks. Independently Published, 2018.

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27

Python Deep Learning: Exploring Deep Learning Techniques and Neural Network Architectures with PyTorch. Packt Publishing, Limited, 2023.

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28

Learn Keras for Deep Neural Networks. Springer Nature, 2019.

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29

Sze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. Efficient Processing of Deep Neural Networks. Morgan & Claypool Publishers, 2020.

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30

Strong, Christopher, Clark Barrett, Changliu Liu, Tomer Arnon, and Christopher Lazarus. Algorithms for Verifying Deep Neural Networks. Now Publishers, 2021.

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31

Sze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. Efficient Processing of Deep Neural Networks. Morgan & Claypool Publishers, 2020.

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32

Sze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. Efficient Processing of Deep Neural Networks. Springer International Publishing AG, 2020.

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33

Sze, Vivienne, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer. Efficient Processing of Deep Neural Networks. Morgan & Claypool Publishers, 2020.

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34

Chen, Yu‐Hsin. Efficient Processing of Deep Neural Networks. Springer Nature, 2020.

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35

Holden, Arun V. Neural Networks - Theory and Architecture. John Wiley & Sons, 1992.

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36

Luigi Mazzeo, Pier, Srinivasan Ramakrishnan, and Paolo Spagnolo, eds. Visual Object Tracking with Deep Neural Networks. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.80142.

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37

Visual Object Tracking with Deep Neural Networks. IntechOpen, 2019.

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38

Aggarwal, Charu C. Neural Networks and Deep Learning: A Textbook. Springer, 2019.

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39

Chang, Dong Eui, and Anthony L. L. Caterini. Deep Neural Networks in a Mathematical Framework. Springer, 2018.

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40

Sugomori, Yusuke, Bostjan Kaluza, Fabio M. Soares, and Alan M. F. Souza. Deep Learning: Practical Neural Networks with Java. Packt Publishing, 2017.

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41

Neural Networks and Deep Learning: A Textbook. Springer, 2018.

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42

Neural Networks and Deep Learning: A Textbook. Springer International Publishing AG, 2023.

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43

Caterini, Anthony L. Deep Neural Networks in a Mathematical Framework. Springer Nature, 2018.

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44

Python Deep Learning: Exploring Deep Learning Techniques and Neural Network Architectures with Pytorch, Keras, and TensorFlow. de Gruyter GmbH, Walter, 2019.

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45

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow. 2nd ed. Packt Publishing, 2019.

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46

Principles Of Artificial Neural Networks: Basic Designs To Deep Learning. World Scientific, 2019.

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47

Wechsler, Harry. Neural Networks for Perception: Computation, Learning, and Architecture (Neural Networks for Perception). Academic Pr, 1991.

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48

Wechsler, Harry. Neural Networks for Perception: Computation, Learning, and Architecture (Neural Networks for Perception). Academic Pr, 1991.

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49

Spencer, Quinn. Neural Networks: Deep Learning and Machine Learning Outlined. Independently Published, 2018.

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50

Evolutionary Deep Learning: Genetic Algorithms and Neural Networks. Manning Publications Co. LLC, 2022.

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