검색어 : 통합검색[Introduction to deep learning :]
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41
- A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees
- Wiggins, Walter F.; Caton Jr, M. Travis; Magudia, Kirti; Rosenthal, Michael H.; Andriole, Katherine P.; ; (Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, v.34, 2021, pp.1026-1033)
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42
- Introduction to the special issue on deep learning for remote sensing environments
- Manogaran, Gunasekaran; Qudrat-Ullah, Hassan; Rawal Kshatriya, Bharat S.; University of California, Davis, CA, USA; York University, Toronto, Canada; Pennsylvsania State University, State College, PA, USA; (European journal of remote sensing, v.53, 2020, pp.1-3)
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43
- An introduction to deep learning on biological sequence data: examples and solutions
- Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae; Department of Bio and Health Informatics; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark; Department of Bio and Health Informatics; Department of Bio and Health Informatics; Department of Bio and Health Informatics; Department of Biology, University of Copenhagen, Copenhagen, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark; Department of Biology, University of Copenhagen, Copenhagen, Denmark; (Bioinformatics, v.33, 2017, pp.3685-3690)
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44
- An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
- Anzalone, Anna; Pagliaro, Antonio; Tutone, Antonio; Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy; Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy; Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy; (Applied sciences, v.14, 2024, pp.2887)
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45
- Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks
- Menke, Janosch; Homberg, Samuel; Koch, Oliver; Institute of Pharmaceutical and Medicinal Chemistry Westfä lische Wilhelms‐ Universitä t Mü nster Mü nster Germany; Institute of Pharmaceutical and Medicinal Chemistry Westfä lische Wilhelms‐ Universitä t Mü nster Mü nster Germany; Institute of Pharmaceutical and Medicinal Chemistry Westfä lische Wilhelms‐ Universitä t Mü nster Mü nster Germany; (Archiv der Pharmazie, v.356, 2023, pp.2200628)
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46
- Introduction to JSTQE Issue on Photonics for Deep Learning and Neural Computing
- Prucnal, Paul R.; Shastri, Bhavin J.; Fischer, Ingo; Brunner, Daniel; Department of Electrical Engineering Princeton University, Princeton, USA; Queen's University, Department of Physics, Engineering Physics & Astronomy, Kingston, Canada; Institute for Cross-Disciplinary Physics and Complex Systems IFISC (CSIC-UIB) Universitat de les Illes Balears, Palma de Mallorca, Spain; Institut FEMTO-ST, UMR CNRS 6174, Dé partement d'Optique, Besanç on, France; (IEEE journal of selected topics in quantum electronics : a publication of the IEEE Communications Society, v.26, 2020, pp.1-3)
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47
- Introduction to Deep Learning by using R
- Ma Jinghao; Song Wanqiu; Izumi Sotaro; Waseda university; Waseda university; Waseda university; (The Proceedings of the Annual Convention of the Japanese Psychological Association, v.84, 2020, pp.TWS-001-TWS-001)
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48
- An introduction to deep learning in medical physics: advantages, potential, and challenges
- Shen, Chenyang; Nguyen, Dan; Zhou, Zhiguo; Jiang, Steve B; Dong, Bin; Jia, Xun; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America; Beijing International Center for Mathematical Research (BICMR), Peking University, Beijing, People’s Republic of China; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas South; (Physics in medicine & biology, v.65, 2020, pp.05TR01)
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49
- Introduction to the special section on deep learning-based intelligent systems (VSI-dlis)
- Huang, Feiran; Mumtaz, Shahid; ; (Computers & electrical engineering, v.92, 2021, pp.107148)
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50
- Introduction to the special issue Deep learning for analysis and synthesis in electromagnetics
- Mognaschi, Maria Evelina; University of Pavia, , Italy E-mail: eve.mognaschi@unipv.it; (International journal of applied electromagnetics and mechanics, v.73, 2023, pp.235-236)
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