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Books on the topic 'Self-supervised learning (artificial intelligence)'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

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

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2

Kanerva, Pentti. The organization of an autonomous learning system. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

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3

He, Haibo. Self-adaptive systems for machine intelligence. Wiley-Interscience, 2011.

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4

Ekici, Berk. Towards self-sufficient high-rises: Performance optimisation using artificial intelligence. BK Books, 2022.

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5

Najim, K. Learning automata: Theory and applications. Pergamon, 1994.

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6

Wang, Huaiqing. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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7

Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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8

Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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9

Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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10

Klimenko, A. V. Osnovy estestvennogo intellekta: Rekurrentnai͡a︡ teorii͡a︡ samoorganizat͡s︡ii : versii͡a︡ 3. Izd-vo Rostovskogo universiteta, 1994.

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11

Hastie, Trevor. The elements of statistical learning: Data mining, inference, and prediction. Springer, 2001.

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12

Heuer, Herbert. Motor Behavior: Programming, Control, and Acquisition. Springer Berlin Heidelberg, 1985.

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13

Robert, Tibshirani, and Friedman J. H, eds. The elements of statistical learning: Data mining, inference, and prediction : with 200 full-color illustrations. Springer, 2001.

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14

Acuña, Ana Isabel González. Contributions to unsupervised and supervised learning with applications in digital image processing: Dissertation presented to the Department Of Computer Science and Artificial Intelligence in partial fulfillment of the requeriments for the degree of Doctor of Philosophy. Universidad del País Vasco, Servicio Editorial = Euskal Herriko Unibertsitatea, Argitalpen Zerbitzua, 2012.

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15

Trevor, Hastie, Tibshirani Robert, and SpringerLink (Online service), eds. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag New York, 2009.

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16

Partially Supervised Learning. Springer-Verlag Berlin and Heidelberg GmbH &, 2012.

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17

Goldberg, Andrew, and Xiaojin Zhu. Introduction to Semi-supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning). Morgan & Claypool Publishers, 2008.

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18

Bateman, Blaine, Benjamin Johnston, Ishita Mathur, and Ashish Ranjan Jha. the Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition. Packt Publishing, Limited, 2020.

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19

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

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20

Vallor, Shannon, and George A. Bekey. Artificial Intelligence and the Ethics of Self-Learning Robots. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190652951.003.0022.

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The convergence of robotics technology with the science of artificial intelligence is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors. Recent advances in machine learning techniques have produced artificial agents that can acquire highly complex skills formerly thought to be the exclusive province of human intelligence. These developments raise a host of new ethical concerns about the responsible design, manufacture, and use of robots enabled with artificial intelligence—particularly those equipped with self-learning capacities. While the pot

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21

Applications Of Supervised And Unsupervised Ensemble Methods. Springer, 2009.

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22

Sawarkar, Kunal, and Dheeraj Arremsetty. Deep Learning with PyTorch Lightning: Build and Train High-Performance Artificial Intelligence and Self-Supervised Models Using Python. Packt Publishing, Limited, 2021.

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23

Proudfoot, Diane, and B. Jack Copeland. Artificial Intelligence. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.

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In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article inMind, ‘Computing Machinery a

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24

Self-Service AI with Power BI Desktop: Machine Learning Insights for Business. Apress L. P., 2020.

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25

Najim, K., and A. S. Poznyak. Learning Automata: Theory and Applications. Elsevier Science & Technology Books, 2014.

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26

Villmann, Thomas, Frank-Michael Schleif, Marika Kaden, and Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4 2014. Springer London, Limited, 2014.

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27

Eliot, Lance. Improving AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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28

Eliot, Lance. Revelatory AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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29

Eliot, Lance. AI Self-Driving Cars Imminence: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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30

Eliot, Lance. Edifying AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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31

Villmann, Thomas, Frank-Michael Schleif, Marika Kaden, and Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, ... in Intelligent Systems and Computing). Springer, 2014.

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32

Lanham, Micheal. Hands-On Reinforcement Learning for Games: Implementing Self-Learning Agents in Games Using Artificial Intelligence Techniques. Packt Publishing, Limited, 2020.

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33

Dutta, Sayon. Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym. Packt Publishing, 2018.

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34

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Limited, John, 2012.

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35

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

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36

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

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37

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

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38

Geraci, Robert M. Futures of Artificial Intelligence. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9788194831679.001.0001.

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Twenty-first-century life is increasingly governed by artificial intelligence (AI) technologies such as machine learning, big data analysis, facial recognition, and robotics. For decades, an ideology of apocalyptic progress and cosmic transformation has accompanied the advancement of AI in the U.S.; that vision is intimately connected to transhumanism—the idea that humanity can transcend its limits, even mortality, using technology. Based on contributions from science and science fiction, advocates of such Apocalyptic AI suggest that the world will soon see godlike machine intelligence and tha

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39

Eliot, Dr Lance. Transformative Artificial Intelligence Driverless Self-Driving Cars: Practical Advances in AI and Machine Learning. LBE Press Publishing, 2018.

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40

Menshawy, Ahmed. Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks. Packt Publishing - ebooks Account, 2018.

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41

Eliot, Dr Lance. Disruptive Artificial Intelligence and Driverless Self-Driving Cars: Practical Advances in Machine Learning and AI. LBE Press Publishing, 2018.

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42

Eliot, Dr Lance. Revolutionary Innovations of AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2018.

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43

Wodecki, Andrzej. Artificial Intelligence in Management: Self-Learning and Autonomous Systems As Key Drivers of Value Creation. Elgar Publishing Limited, Edward, 2020.

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44

Zhou, Zhi-Hua, and Friedhelm Schwenker. Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers. Springer, 2013.

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45

Zhou, Zhi-Hua, and Friedhelm Schwenker. Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers. Springer Berlin / Heidelberg, 2013.

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46

Partially Supervised Learning: First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers. Springer, 2012.

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47

Eliot, Dr Lance, and Michael Eliot. Autonomous Vehicle Driverless Self-Driving Cars and Artificial Intelligence: Practical Advances in AI and Machine Learning. LBE Press Publishing, 2017.

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48

The Elements of Statistical Learning. Springer, 2008.

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49

Waildŭ: Song In-sŏp Kyosu ŭi A·I sidae kamsŏng ch'angjo kyoyukpŏp = Wild. Tasan Edyu, 2020.

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50

Advances in AI and Autonomous Vehicles : Cybernetic Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. Lbe Press Publishing, 2017.

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