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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289547.pdf · MOOCs and online courses have notoriously high attrition [1]. One challenge is ... a student's performance
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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are
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web.stanford.eduweb.stanford.edu/class/cs230/projects_spring_2018/... · 50 image as input, and generate a higher resolution 250 x 250 output image. 2. Related Work This project was
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cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12449174.pdf · YOLO ensembles performs marginally better than YOLO as a single model. In addition, some steps ofChexNet
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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15782416.pdf · analysis of the models results can be found in the Discussion section. ... and a recognizing textual
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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8270111.pdf · With our efforts through this quarter, we have successfully built a speaker identification algorithm
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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811654.pdf · 2019-04-04 · Using preprocessing code provided by Kuleshov et al.'s GitHub repositoryl , I generated
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CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/posters/8285188.pdfExplore and develop a deep machine learning model that predicts the future price of digital asset such
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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681189.pdfMore broadly our project is part of the growing field of object detection and classification. A future
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