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Livros sobre o tema "Deep learning with uncertainty"

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Autor: Grafiati

Publicado: 7 de julho de 2024

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1

Marchau, Vincent A. W. J., Warren E. Walker, Pieter J. T. M. Bloemen e Steven W. Popper, eds. Decision Making under Deep Uncertainty. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2.

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2

Saefken, Benjamin, Alexander Silbersdorff e Christoph Weisser, eds. Learning deep. Göttingen: Göttingen University Press, 2020. http://dx.doi.org/10.17875/gup2020-1338.

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3

Bishop, Christopher M., e Hugh Bishop. Deep Learning. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45468-4.

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4

Kruse, René-Marcel, Benjamin Säfken, Alexander Silbersdorff e Christoph Weisser, eds. Learning Deep Textwork. Göttingen: Göttingen University Press, 2021. http://dx.doi.org/10.17875/gup2021-1608.

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5

Rodriguez, Andres. Deep Learning Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8.

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6

Fergus, Paul, e Carl Chalmers. Applied Deep Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04420-5.

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7

Calin, Ovidiu. Deep Learning Architectures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3.

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8

El-Amir, Hisham, e Mahmoud Hamdy. Deep Learning Pipeline. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5349-6.

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9

Matsushita, Kayo, ed. Deep Active Learning. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5660-4.

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10

Michelucci, Umberto. Applied Deep Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.

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11

Moons, Bert, Daniel Bankman e Marian Verhelst. Embedded Deep Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99223-5.

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12

Wani, M. Arif, Mehmed Kantardzic e Moamar Sayed-Mouchaweh, eds. Deep Learning Applications. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1816-4.

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13

Dong, Hao, Zihan Ding e Shanghang Zhang, eds. Deep Reinforcement Learning. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0.

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14

Kim, Phil. MATLAB Deep Learning. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2845-6.

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15

Sewak, Mohit. Deep Reinforcement Learning. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8285-7.

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16

Gamba, Jonah. Deep Learning Models. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9672-8.

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17

Jo, Taeho. Deep Learning Foundations. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4.

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18

Singaram, Jayakumar, S. S. Iyengar e Azad M. Madni. Deep Learning Networks. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-39244-3.

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19

Enrique, Castillo. Expert systems: Uncertainty and learning. Southampton: Computational Mechanics, 1991.

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20

Hu, Fei, e Xiali Hei. AI, Machine Learning and Deep Learning. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158.

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21

Ketkar, Nikhil, e Jojo Moolayil. Deep Learning with Python. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-5364-9.

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22

Kim, Kwangjo, e Harry Chandra Tanuwidjaja. Privacy-Preserving Deep Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3764-3.

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23

Benois-Pineau, Jenny, e Akka Zemmari, eds. Multi-faceted Deep Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74478-6.

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24

Ye, Jong Chul. Geometry of Deep Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6046-7.

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25

Ahmed, Khaled R., e Henry Hexmoor, eds. Blockchain and Deep Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95419-2.

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26

Betti, Alessandro, Marco Gori e Stefano Melacci. Deep Learning to See. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1.

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27

Betti, Alessandro, Marco Gori e Stefano Melacci. Deep Learning to See. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1.

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28

Paluszek, Michael, Stephanie Thomas e Eric Ham. Practical MATLAB Deep Learning. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7912-0.

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29

Wani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal e Asif Iqbal Khan. Advances in Deep Learning. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-6794-6.

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30

Michelucci, Umberto. Advanced Applied Deep Learning. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4976-5.

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31

Paluszek, Michael, e Stephanie Thomas. Practical MATLAB Deep Learning. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5124-9.

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32

Salvaris, Mathew, Danielle Dean e Wee Hyong Tok. Deep Learning with Azure. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3679-6.

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33

Bhanu, Bir, e Ajay Kumar, eds. Deep Learning for Biometrics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5.

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34

Ghatak, Abhijit. Deep Learning with R. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5850-0.

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35

Skansi, Sandro. Introduction to Deep Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73004-2.

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36

Ketkar, Nikhil. Deep Learning with Python. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2766-4.

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37

Amaratunga, Thimira. Deep Learning on Windows. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6431-7.

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38

Chen, Yen-Wei, e Lakhmi C. Jain, eds. Deep Learning in Healthcare. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32606-7.

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39

Tanaka, Akinori, Akio Tomiya e Koji Hashimoto. Deep Learning and Physics. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9.

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40

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

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Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.

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41

Tutino, Stefania. Uncertainty in Post-Reformation Catholicism. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190694098.001.0001.

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This book provides a historical account of the development and implications of early modern probabilism. First elaborated in the sixteenth century, probabilism represented a significant and controversial novelty in Catholic moral theology. Against a deep-seated tradition defending the strict application of moral rules, probabilist theologians maintained that in situations of uncertainty, the agent can legitimately follow any course of action supported by a probable opinion, no matter how disputable. By the second half of the seventeenth century, and thanks in part to Pascal’s influential antiprobabilist stances, probabilism had become inextricably linked to the Society of Jesus and to a lax and excessively forgiving moral system. To this day, most historians either ignore probabilism, or they associate it with moral duplicity and intellectual and cultural decadence. By contrast, this book argues that probabilism was instrumental for addressing the challenges created by a geographically and intellectually expanding world. Early modern probabilist theologians saw that these challenges provoked an exponential growth of uncertainties, doubts, and dilemmas of conscience, and they realized that traditional theology was not equipped to deal with them. Therefore, they used probabilism to integrate changes and novelties within the post-Reformation Catholic theological and intellectual system. Seen in this light, probabilism represented the result of their attempts to appreciate, come to terms with, and manage uncertainty. Uncertainty continues to play a central role even today. Thus, learning how early modern probabilists engaged with uncertainty might be useful for us as we try to cope with our own moral and epistemological doubts.

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42

Walker, Warren E., Steven W. Popper e Pieter J T M Bloemen. Decision Making Under Deep Uncertainty. Saint Philip Street Press, 2020.

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43

Walker, Warren E., Steven W. Popper e Pieter J T M Bloemen. Decision Making Under Deep Uncertainty. Saint Philip Street Press, 2020.

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44

Wang, Xizhao, e Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.

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45

Wang, Xizhao, e Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.

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46

Wang, Xizhao, e Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2020.

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47

Wang, Xizhao, e Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.

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48

Wang, Xizhao, e Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.

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49

Wang, Xizhao, e Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.

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50

Kelleher, John D. Deep Learning. MIT Press, 2019.

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