Books on the topic 'Deep learning with uncertainty'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 books for your research on the topic 'Deep learning with uncertainty.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse books on a wide variety of disciplines and organise your bibliography correctly.
Marchau, Vincent A. W. J., Warren E. Walker, Pieter J. T. M. Bloemen, and 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.
Saefken, Benjamin, Alexander Silbersdorff, and Christoph Weisser, eds. Learning deep. Göttingen: Göttingen University Press, 2020. http://dx.doi.org/10.17875/gup2020-1338.
Bishop, Christopher M., and Hugh Bishop. Deep Learning. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45468-4.
Kruse, René-Marcel, Benjamin Säfken, Alexander Silbersdorff, and Christoph Weisser, eds. Learning Deep Textwork. Göttingen: Göttingen University Press, 2021. http://dx.doi.org/10.17875/gup2021-1608.
Rodriguez, Andres. Deep Learning Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8.
Fergus, Paul, and Carl Chalmers. Applied Deep Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04420-5.
Calin, Ovidiu. Deep Learning Architectures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3.
El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5349-6.
Matsushita, Kayo, ed. Deep Active Learning. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5660-4.
Michelucci, Umberto. Applied Deep Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.
Moons, Bert, Daniel Bankman, and Marian Verhelst. Embedded Deep Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99223-5.
Wani, M. Arif, Mehmed Kantardzic, and Moamar Sayed-Mouchaweh, eds. Deep Learning Applications. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1816-4.
Dong, Hao, Zihan Ding, and Shanghang Zhang, eds. Deep Reinforcement Learning. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0.
Kim, Phil. MATLAB Deep Learning. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2845-6.
Sewak, Mohit. Deep Reinforcement Learning. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8285-7.
Gamba, Jonah. Deep Learning Models. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9672-8.
Jo, Taeho. Deep Learning Foundations. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4.
Singaram, Jayakumar, S. S. Iyengar, and Azad M. Madni. Deep Learning Networks. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-39244-3.
Enrique, Castillo. Expert systems: Uncertainty and learning. Southampton: Computational Mechanics, 1991.
Hu, Fei, and Xiali Hei. AI, Machine Learning and Deep Learning. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158.
Ketkar, Nikhil, and Jojo Moolayil. Deep Learning with Python. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-5364-9.
Kim, Kwangjo, and Harry Chandra Tanuwidjaja. Privacy-Preserving Deep Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3764-3.
Benois-Pineau, Jenny, and Akka Zemmari, eds. Multi-faceted Deep Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74478-6.
Ye, Jong Chul. Geometry of Deep Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6046-7.
Ahmed, Khaled R., and Henry Hexmoor, eds. Blockchain and Deep Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95419-2.
Betti, Alessandro, Marco Gori, and Stefano Melacci. Deep Learning to See. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1.
Betti, Alessandro, Marco Gori, and Stefano Melacci. Deep Learning to See. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1.
Paluszek, Michael, Stephanie Thomas, and Eric Ham. Practical MATLAB Deep Learning. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7912-0.
Wani, M. Arif, Farooq Ahmad Bhat, Saduf Afzal, and Asif Iqbal Khan. Advances in Deep Learning. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-6794-6.
Michelucci, Umberto. Advanced Applied Deep Learning. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4976-5.
Paluszek, Michael, and Stephanie Thomas. Practical MATLAB Deep Learning. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5124-9.
Salvaris, Mathew, Danielle Dean, and Wee Hyong Tok. Deep Learning with Azure. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3679-6.
Bhanu, Bir, and Ajay Kumar, eds. Deep Learning for Biometrics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61657-5.
Ghatak, Abhijit. Deep Learning with R. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5850-0.
Skansi, Sandro. Introduction to Deep Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73004-2.
Ketkar, Nikhil. Deep Learning with Python. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2766-4.
Amaratunga, Thimira. Deep Learning on Windows. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6431-7.
Chen, Yen-Wei, and Lakhmi C. Jain, eds. Deep Learning in Healthcare. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32606-7.
Tanaka, Akinori, Akio Tomiya, and Koji Hashimoto. Deep Learning and Physics. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9.
Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.
Tutino, Stefania. Uncertainty in Post-Reformation Catholicism. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190694098.001.0001.
Walker, Warren E., Steven W. Popper, and Pieter J T M Bloemen. Decision Making Under Deep Uncertainty. Saint Philip Street Press, 2020.
Walker, Warren E., Steven W. Popper, and Pieter J T M Bloemen. Decision Making Under Deep Uncertainty. Saint Philip Street Press, 2020.
Wang, Xizhao, and Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.
Wang, Xizhao, and Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.
Wang, Xizhao, and Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2020.
Wang, Xizhao, and Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.
Wang, Xizhao, and Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.
Wang, Xizhao, and Junhai Zhai. Learning with Uncertainty. Taylor & Francis Group, 2016.
Kelleher, John D. Deep Learning. MIT Press, 2019.