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1

Nandi, Anirban, e Aditya Kumar Pal. Interpreting Machine Learning Models. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4.

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2

Bolc, Leonard. Computational Models of Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987.

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3

Galindez Olascoaga, Laura Isabel, Wannes Meert e Marian Verhelst. Hardware-Aware Probabilistic Machine Learning Models. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74042-9.

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4

Singh, Pramod. Deploy Machine Learning Models to Production. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6546-8.

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5

Zhang, Zhihua. Statistical Machine Learning: Foundations, Methodologies and Models. UK: John Wiley & Sons, Limited, 2017.

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6

Rendell, Larry. Representations and models for concept learning. Urbana, IL (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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7

Ehteram, Mohammad, Zohreh Sheikh Khozani, Saeed Soltani-Mohammadi e Maliheh Abbaszadeh. Estimating Ore Grade Using Evolutionary Machine Learning Models. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8106-7.

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8

Bisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.

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9

Gupta, Punit, Mayank Kumar Goyal, Sudeshna Chakraborty e Ahmed A. Elngar. Machine Learning and Optimization Models for Optimization in Cloud. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003185376.

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10

Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3.

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11

Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Hershey, PA: Information Science Reference, 2010.

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12

Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Hershey, PA: Information Science Reference, 2010.

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13

Rasmussen, Carl Edward. Gaussian processes for machine learning. Cambridge, MA: MIT Press, 2005.

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14

Nandi, Anirban, e Aditya Kumar Pal. Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods. Apress L. P., 2022.

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15

Bhattacharya, Aditya. Applied Machine Learning Explainability Techniques: Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More. Packt Publishing, Limited, 2022.

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16

Bolc, Leonard. Computational Models of Learning. Springer, 2011.

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17

Croman, Chasity. Tutorials on Machine Learning: Start Learning Machine Learning and Build Your Own Models. Independently Published, 2022.

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18

Xin, Liu, Ee-Peng Lim e Anwitaman Datta. Computational Trust Models and Machine Learning. Taylor & Francis Group, 2014.

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19

Xin, Liu, Ee-Peng Lim e Anwitaman Datta. Computational Trust Models and Machine Learning. Taylor & Francis Group, 2020.

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20

N, Ambika. Building Business Models with Machine Learning. IGI Global, 2024.

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21

Adversarial Robustness for Machine Learning Models. Elsevier Science & Technology Books, 2022.

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22

Explainable Machine Learning Models and Architectures. Wiley & Sons, Incorporated, John, 2023.

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23

Computational trust models and machine learning. Boca Raton: Taylor & Francis, 2014.

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24

Explainable Machine Learning Models and Architectures. Wiley & Sons, Incorporated, John, 2023.

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25

Mehtab, Sidra, e Jaydip Sen. Machine Learning: Algorithms, Models and Applications. IntechOpen, 2021.

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26

Xin, Liu, Ee-Peng Lim e Anwitaman Datta. Computational Trust Models and Machine Learning. Taylor & Francis Group, 2014.

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27

Xin, Liu, Ee-Peng Lim e Anwitaman Datta. Computational Trust Models and Machine Learning. Taylor & Francis Group, 2014.

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28

Chen, Gang. Machine Learning: Basics, Models and Trends. Independently Published, 2017.

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29

N, Ambika. Building Business Models with Machine Learning. IGI Global, 2024.

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30

Practical MLOps: Operationalizing Machine Learning Models. O'Reilly Media, Incorporated, 2021.

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31

N, Ambika. Building Business Models with Machine Learning. IGI Global, 2024.

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32

Explainable Machine Learning Models and Architectures. Wiley & Sons, Incorporated, John, 2023.

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33

N, Ambika. Building Business Models with Machine Learning. IGI Global, 2024.

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34

N, Ambika. Building Business Models with Machine Learning. IGI Global, 2024.

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35

Adversarial Robustness for Machine Learning Models. Elsevier Science & Technology, 2022.

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36

Wineinger, Hubert. Python Book : How to Build Predictive Machine Learning Models Step by Step: Machine Learning Models. Independently Published, 2021.

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37

Generalized Low Rank Models. 2016.

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38

Generalized Low Rank Models. Now Publishers, 2016.

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39

Yeaman, Kym. Machine Learning for Beginners : Code Basic Machine Learning Models Using Python: Introduction to Machine Learning with Python. Independently Published, 2021.

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40

Computational models of learning. Berlin: Springer-Verlag, 1987.

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41

Madani, Ali. Debugging Machine Learning Models with Python: Develop High-Performance, Low-bias, and Explainable Machine Learning and Deep Learning Models. de Gruyter GmbH, Walter, 2023.

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42

Statistical Machine Learning: Foundations, Methodologies and Models. UK: Wiley-Blackwell (an imprint of John Wiley & Sons Ltd), 2020.

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43

Explainable Machine Learning Models and Architectu Res. Wiley & Sons, Limited, John, 2023.

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44

Agrawal, Tanay. Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. Apress L. P., 2020.

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45

Sammons, Mark, Dan Roth, Fabio Zanzotto e Ido Dagan. Recognizing Textual Entailment: Models and Applications. Springer International Publishing AG, 2013.

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46

Sammons, Mark, Dan Roth, Fabio Zanzotto e Ido Dagan. Recognizing Textual Entailment: Models and Applications. Morgan & Claypool Publishers, 2013.

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47

Sammons, Mark, Dan Roth, Fabio Zanzotto e Ido Dagan. Recognizing Textual Entailment: Models and Applications. Morgan & Claypool Publishers, 2013.

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48

Machine Learning with Pytorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python. Packt Publishing, Limited, 2022.

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49

Machine Learning with Pytorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python. de Gruyter GmbH, Walter, 2022.

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50

Vidales, A. Machine Learning with Matlab: Supervised Learning Using Predictive Models. Regression. Independently Published, 2019.

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