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CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches
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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_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation
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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question
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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18676218.pdfProblem Statement: The purpose of this project was to create a system - based on neural networks - that
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cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6908505.pdf · In this project, we build three deep learning models (DenseNet-121, DenseNet- LSTM and DenseNet-GRU)
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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8288946.pdf · jazz piano piece). It was converted into a text file, which contains its noteOn, noteOff, control
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CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12449275.pdf · Amita C. Patil and Rudra S. Bandhu Department of Computer Science Stanford University (amita2, . edu
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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681331.pdf · for a specific digit in a "hand written digit recognition problem". This may lead to an inaccurate
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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/8288669.pdf · Hiro Tien (Kai Ping) Stanford Graduate School of Business Stanford School of Earth, Energy & Environmental
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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8284387.pdf · 2018-09-28 · these phrases are consistent with the English language, the output does not represent
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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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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681243.pdf · connected layers to obtain their object category and confidence level. We keep all the patches with
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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681618.pdf · Tool detection:Used Fast-RCNN for spatial detection of surgical tools and VGG16 for classification
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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813330.pdf · According to the Federal Statistics Office, 2013, the number of newly opened insolvency proceedings
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CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12437786.pdf · ANET achieved 0.87 recall rate across all test cases. CS230: Deep Learning, Fall 2018, Stanford University,
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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18680194.pdf · capture short term trends in the market under the set of assumptions they impose on the underlying
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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802276.pdf · each artist. The resulting model attained good performance over the baseline, and provided subjectively
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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)
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