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Books on the topic 'Generative adversarial networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 23 November 2024

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1

Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6048-8.

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2

Raut, Roshani, Pranav D Pathak, Sachin R Sakhare, and Sonali Patil. Generative Adversarial Networks and Deep Learning. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003203964.

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3

Kaddoura, Sanaa. A Primer on Generative Adversarial Networks. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32661-5.

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4

Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Springer Singapore Pte. Limited, 2022.

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5

Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Springer Singapore Pte. Limited, 2021.

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6

Kaddoura, Sanaa. Primer on Generative Adversarial Networks. Springer International Publishing AG, 2023.

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7

Generative Adversarial Networks in Practice. CRC Press LLC, 2024.

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8

Generative Adversarial Networks in Practice. CRC Press LLC, 2023.

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9

Valle, Rafael. Hands-On Generative Adversarial Networks with Keras: Your Guide to Implementing Next-Generation Generative Adversarial Networks. Packt Publishing, Limited, 2019.

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10

Ahirwar, Kailash. Generative Adversarial Networks Projects: Build Next-Generation Generative Models Using TensorFlow and Keras. Packt Publishing, Limited, 2019.

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11

Solanki, Arun, Anand Nayyar, and Mohd Naved. Generative Adversarial Networks for Image-To-Image Translation. Elsevier Science & Technology Books, 2021.

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12

Generative Adversarial Networks for Image-to-Image Translation. Elsevier, 2021. http://dx.doi.org/10.1016/c2020-0-00284-7.

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13

Solanki, Arun, Anand Nayyar, and Mohd Naved. Generative Adversarial Networks for Image-To-Image Translation. Elsevier Science & Technology, 2021.

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14

Langr, Jakub, and Vladimir Bok. GANs in Action: Deep Learning with Generative Adversarial Networks. Manning Publications Co. LLC, 2019.

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15

Generative Adversarial Networks and Deep Learning: Theory and Applications. CRC Press LLC, 2023.

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16

Sakhare, Sachin R., Pranav D. Pathak, Sonali Patil, and Roshani Raut. Generative Adversarial Networks and Deep Learning: Theory and Applications. CRC Press LLC, 2023.

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17

Sakhare, Sachin R., Pranav D. Pathak, Sonali Patil, and Roshani Raut. Generative Adversarial Networks and Deep Learning: Theory and Applications. CRC Press LLC, 2023.

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18

Sakhare, Sachin R., Pranav D. Pathak, Sonali Patil, and Roshani Raut. Generative Adversarial Networks and Deep Learning: Theory and Applications. CRC Press LLC, 2023.

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19

GANs in Action: Deep Learning with Generative Adversarial Networks. Manning Publications Company, 2019.

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20

Generative Adversarial Networks and Deep Learning: Theory and Applications. CRC Press LLC, 2023.

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21

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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22

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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23

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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24

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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25

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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26

Sethi, Dinesh, Manish Jain, and Loveleen Kumar. Generative Adversarial Networks and Super Resolution: A Machine Learning Approach. Wiley & Sons, Incorporated, John, 2022.

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27

Sethi, Dinesh, Manish Jain, and Loveleen Kumar. Generative Adversarial Networks and Super Resolution: A Machine Learning Approach. Wiley & Sons, Incorporated, John, 2022.

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28

Sethi, Dinesh, Manish Jain, and Loveleen Kumar. Generative Adversarial Networks and Super Resolution: A Machine Learning Approach. Wiley & Sons, Limited, John, 2022.

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29

Sethi, Dinesh, Manish Jain, and Loveleen Kumar. Generative Adversarial Networks and Super Resolution: A Machine Learning Approach. Wiley & Sons, Incorporated, John, 2022.

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30

Ketkar, Nihkil. Practical Deep Learning with PyTorch: Optimizing Generative Adversarial Networks with Python. Apress, 2020.

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31

Generative Adversarial Networks Cookbook: Over 100 Recipes to Build Generative Models Using Python, TensorFlow, and Keras. de Gruyter GmbH, Walter, 2018.

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32

Kalin, Josh. Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras. Packt Publishing, 2018.

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33

Walters, Greg, and John Hany. Hands-On Generative Adversarial Networks with Pytorch 1. x: Implement Next-Generation Neural Networks to Build Powerful GAN Models Using Python. Packt Publishing, Limited, 2019.

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34

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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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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35

Fahrerassistenzsysteme und automatisiertes Fahren. VDI Verlag, 2022. http://dx.doi.org/10.51202/9783181023945.

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Inhalt Pitch der Innovationen – Impulsvorträge im Plenummit anschließender Poster-Session Manipulation von Sensordaten aus Testfahrten zur Analyse und Bewertung implementierter Rückfalllösungen . . . . . . . . . . . . . .1 Sensortechnologien und Perzeption Radar Target Simulator – Key Technology for AV Development . . . . . . . . . . . 13 Künstliche Intelligenz (KI), Verhaltensplanung und Kooperation Realisierung einer querführenden Fahrerassistenzfunktion mithilfe von adaptiver Regelung und neuronalen Netzen . . .. . . . . . .27 Augmentation von Kameradaten mit Generative Adversarial Networks (GANs) zur Absicherung automatisierter Fahrfunktionen . . . . . . . . . . . 41 Kalibrierung von Neuronalen Netzen für Detektionsmodelle . .. . . . . . . 49 Projekt COPE – Collective Perception zur Vermeidung von Kollisionen und gefährlichen Situationen mittels V2X . . . . . . . .63 Architekturen für voll- und teilautomatisiertes Fahren UNICARagil – Disruptive Modular Architectures for Agile Automated Vehicle Concepts . . . . . . 75 Ein industrieübergreifender Überblick von fehlertoleranten Ansätze...

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36

Lanham, Micheal. Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes. Apress L. P., 2021.

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