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Bibliografias temáticas / Continuous and distributed machine learning / Livros

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Livros sobre o tema "Continuous and distributed machine learning"

Autor: Grafiati

Publicado: 31 de agosto de 2024

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1

Weiss, Gerhard. Distributed machine learning. Sankt Augustin: Infix, 1995.

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2

Testas, Abdelaziz. Distributed Machine Learning with PySpark. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9751-3.

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3

Amini, M. Hadi, ed. Distributed Machine Learning and Computing. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57567-9.

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4

Jiang, Jiawei, Bin Cui e Ce Zhang. Distributed Machine Learning and Gradient Optimization. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3420-8.

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5

Joshi, Gauri. Optimization Algorithms for Distributed Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19067-4.

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6

Sahoo, Jyoti Prakash, Asis Kumar Tripathy, Manoranjan Mohanty, Kuan-Ching Li e Ajit Kumar Nayak, eds. Advances in Distributed Computing and Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6.

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7

Rout, Rashmi Ranjan, Soumya Kanti Ghosh, Prasanta K. Jana, Asis Kumar Tripathy, Jyoti Prakash Sahoo e Kuan-Ching Li, eds. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1018-0.

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8

Tripathy, Asis Kumar, Mahasweta Sarkar, Jyoti Prakash Sahoo, Kuan-Ching Li e Suchismita Chinara, eds. Advances in Distributed Computing and Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-4218-3.

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9

Nanda, Umakanta, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar e Kuan-Ching Li, eds. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1841-2.

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10

Chinara, Suchismita, Asis Kumar Tripathy, Kuan-Ching Li, Jyoti Prakash Sahoo e Alekha Kumar Mishra, eds. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2.

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11

Nanda, Umakanta, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar e Kuan-Ching Li, eds. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3523-5.

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12

Weiß, Gerhard, ed. Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62934-3.

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13

European Conference on Artificial Intelligence (12th 1996 Budapest, Hungary). Distributed artificial intelligence meets machine learning: Learning in multi-agent environments : ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996, selected papers. Berlin: Springer, 1997.

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14

Kim, Steven H. Learning and coordination: Enhancing agent performance through distributed decision making. Dordrecht: Kluwer Academic, 1994.

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15

International, Joint Conference on Artificial Intelligence (14th 1995 Montréal Québec). Adaption and learning in multi-agent systems: IJCAI '95 workshop, Montréal, Canada, August 21, 1995, proceedings. Berlin: Springer, 1996.

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16

Chaturvedi, Alok R. A machine learning approach to the design of time invariant fragments for replication in a distributed database environment. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1989.

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17

Oyarzun Laura, Cristina, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg et al., eds. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90874-4.

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18

1962-, Weiss Gerhard, European Conference on Artificial Intelligence, (12th : 1996 : Budapest, Hungary) e International Conference on Multi-Agent Systems, (2nd : 1996 : Kyoto, Japan), eds. Distributed artificial intelligence meets machine learning: Learning in multi-agent environments : ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996 : selected papers. Berlin: Springer, 1997.

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19

Polyakova, Anna, Tat'yana Sergeeva e Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.

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Resumo:

The material presented in the monograph shows the possibilities of continuous teaching of mathematics at school, namely, the significant potential of modern information and communication technologies, with the help of which it is possible to form elements of stochastic culture among students. Continuity in learning is considered from two positions: procedural and educational-cognitive. In addition, a distinctive feature of the book is the presentation of the digital transformation of general education as a way to overcome the "new digital divide". Methodological features of promising digital technologies (within the framework of teaching students the elements of the probabilistic and statistical line) that contribute to overcoming the "new digital divide": artificial intelligence, the Internet of Things, additive manufacturing, machine learning, blockchain, virtual and augmented reality are described. The solution of the main questions of probability theory and statistics in the 9th grade mathematics course is proposed to be carried out using a distance learning course built in the Moodle distance learning system. The content, structure and methodological features of the implementation of the stochastics course for students of grades 10-11 of a secondary school are based on the use of such tools in the educational process as an online calculator for plotting functions, the Wolfram Alpha service, Google Docs and Google Tables services, the Yaklass remote training, the Banktest website.<url>", interactive module "Galton Board", educational website "Mathematics at school". It will be interesting for students, undergraduates, postgraduates, mathematics teachers, as well as specialists improving their qualifications in the field of pedagogical education.

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20

Leigh, J. R. Control Theory. 2^a ed. Stevenage: IET, 2004.

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21

Distributed Machine Learning Patterns. Manning Publications Co. LLC, 2024.

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22

Tang, Yuan. Distributed Machine Learning Patterns. Manning Publications Co. LLC, 2022.

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23

Optimization Algorithms for Distributed Machine Learning. Springer International Publishing AG, 2023.

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24

Distributed Machine Learning and Gradient Optimization. Springer, 2023.

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25

Distributed Machine Learning and Gradient Optimization. Springer Singapore Pte. Limited, 2021.

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26

Learning Ray: Flexible Distributed Python for Machine Learning. O'Reilly Media, Incorporated, 2023.

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27

Harini, S., e V. Pattabiraman. Scalable and Distributed Machine Learning and Deep Learning Patterns. IGI Global, 2023.

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28

Harini, S., e V. Pattabiraman. Scalable and Distributed Machine Learning and Deep Learning Patterns. IGI Global, 2023.

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29

Harini, S., e V. Pattabiraman. Scalable and Distributed Machine Learning and Deep Learning Patterns. IGI Global, 2023.

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30

Harini, S., e V. Pattabiraman. Scalable and Distributed Machine Learning and Deep Learning Patterns. IGI Global, 2023.

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31

Langford, John, Ron Bekkerman e Mikhail Bilenko. Scaling up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, 2011.

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32

Langford, John, Ron Bekkerman e Mikhail Bilenko. Scaling up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, 2012.

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33

Scaling up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, 2018.

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34

Langford, John, Ron Bekkerman e Mikhail Bilenko. Scaling up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, 2012.

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35

Scaling up machine learning: Parallel and distributed approaches. Cambridge: Cambridge University Press, 2011.

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36

Gupta, Nirupam, e Rafael Pinot. Robust Machine Learning: Distributed Methods for Safe AI. Springer, 2024.

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37

Wang, Guanhua. Distributed Machine Learning with Python: Accelerating Model Training and Serving with Distributed Systems. Packt Publishing, Limited, 2022.

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38

Distributed Machine Learning with Python: Accelerating Model Training and Serving with Distributed Systems. de Gruyter GmbH, Walter, 2022.

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39

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. 2010.

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40

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers. Now Publishers, 2011.

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41

Collier, Rich, e Bahaaldine Azarmi. Machine Learning with the Elastic Stack: Expert Techniques to Integrate Machine Learning with Distributed Search and Analytics. Packt Publishing, Limited, 2019.

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42

Tatarenko, Tatiana. Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems. Springer, 2017.

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43

Tatarenko, Tatiana. Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems. Springer, 2018.

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44

Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021. Springer, 2022.

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45

Li, Kuan-Ching, Asis Kumar Tripathy, Mahasweta Sarkar, Jyoti Prakash Sahoo e Suchismita Chinara. Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2020. Springer, 2020.

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46

Li, Kuan-Ching, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Ajit Kumar Nayak e Manoranjan Mohanty. Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021. Springer Singapore Pte. Limited, 2021.

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47

Rout, Rashmi Ranjan, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Soumya Kanti Ghosh e Prasanta K. Jana. Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2022. Springer, 2022.

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48

Tripathy, Asis Kumar, Mahasweta Sarkar e Jyoti Prakash Sahoo. Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2020. Springer, 2020.

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49

Iozzia, Guglielmo. Hands-On Deep Learning with Apache Spark: Build and Deploy Distributed Deep Learning Applications on Apache Spark. Packt Publishing, Limited, 2019.

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50

Grant, Ginger, Guillermo Fernandez, Julio Granados, Pau Sempere e Javier Torrenteras. Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning. Microsoft Press, 2018.

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