CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290433.pdf · Stanford University {zhaozhuo, zhiyuan8, edu Abstract Damage of building is an essential indicator
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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set.
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
cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802990.pdf · 2019-04-04 · Both regression and classification approaches have been used to address issue of fake
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812659.pdf · 2019. 4. 4. · mean lower IOU for the YOLO model in many cases). We have pre-processed these images
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
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813327.pdf0.024 0.022 0.020 Train 0.029 0.036 0.051 Test 0.048 0.026 0.028 Train 81.19% 87.55% 74.60% Test 85.82%
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,
CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290634.pdf · alternative of rating food photos' attractiveness to Yelp's published approach that utilized EXIF
cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813120.pdf · animated images and applied to images earlier in the creative process. Style images from animated
cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812583.pdfwith 1,716 car models. The full car images are labeled with bounding boxes and viewpoints. Each car model
CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681615.pdfStanford University 1050 Arastradero Rd., Stanford, CA kkaganov [ at ] stanford.edu Abstract In order
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811993.pdfChallenge[91 on Kaggle, which presents a dataset of user submitted photos of restaurants and 9 possible
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813291.pdf · The draft version of the application was written to generate entire trainset/devset up in front,
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812313.pdf · As you can see, Trump. In this case, our mask will be an array of ai this mask turns off vectors
CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15809694.pdfexisting previous piece of artwork in a personalized manner. In our method, we alter an existing piece
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