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Page 1: 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
Page 2: 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
Page 3: 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
Page 4: 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
Page 5: 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
Page 6: 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

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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

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811843.pdfincluding flipping, cropping, rotating, and etc. MOM Figure 1. Sample image of Bart Simpson, Homer Simpson

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15792433.pdf · supermarkets: four Safeways near Palo Alto, SF and one H-E-B in Austin, TX. Between 40 and 60 snapshots

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813172.pdf80/10/10 train, dev, and test splits based on recom- Fig. 1) The Starry Night by Vincent van Gogh [1]

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine learning model. For the audio classifier presented in this work, the inputs to the

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810323.pdfNetworks and Siamese Neural Networks Marios Andreas Galanis, Vladimir Kozlow ... There is a very large body

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15795079.pdf · 2019. 4. 4. · re n [1] Hingmire, S.; Chougule, S., Palshikar, G. K., and Chakraborti, S. 2013

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15791197.pdf3.1 Manga The manga dataset is a subset of Manga109 [9], [8]. Manga109 consists of manga pages from 109

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

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15773386.pdf · camera at any given position and orientation. A random sampling of camera positions is taken within

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 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812222.pdf · 2019-04-04 · Iterative Cloud Point (ICP) with depth information or iterative model matching architecture

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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_winter_2019/reports/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806104.pdf · Description 3 inputs, 1 hidden layer, 100 units 3 inputs, 1 hidden layer, 100 units 4 inputs, 1

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CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

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

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811878.pdf · striker (offensive agent) and goalie (defensive agent), we explore how agents can ... formation

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