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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute prob- lem stemmmg from

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Page 1: CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute problem stemmmg from

Page 2: CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute problem stemmmg from

Page 3: CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute problem stemmmg from

Page 4: CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute problem stemmmg from

Page 5: CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute problem stemmmg from

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

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/files_winter_2018/projects/6940506.pdf · OCR focused on historical transcription has been rarely applied on Arabic historical manuscripts

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940506.pdf · OCR focused on historical transcription has been rarely applied on Arabic historical manuscripts

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18678124.pdf · 2019. 6. 13. · and jet lag . Holidays availed for relaxation . Speech/Lack of speech . Eyes strain

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18678124.pdf · 2019. 6. 13. · and jet lag . Holidays availed for relaxation . Speech/Lack of speech . Eyes strain

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18674643.pdfpate: 1024 Output layer 3X3 5X5 conv, padding same 2X2 Max Pool 7X7 2X2 Average Incept i on • Toxicity

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18674643.pdfpate: 1024 Output layer 3X3 5X5 conv, padding same 2X2 Max Pool 7X7 2X2 Average Incept i on • Toxicity

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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)

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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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_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 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18679304.pdfIn order to infer spatial location through neural data over time, we have adapted an Long Short Term

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18679304.pdfIn order to infer spatial location through neural data over time, we have adapted an Long Short Term

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18641024.pdfGenre Pop Rock Alternative Rock World Music Dance & Electronic Jazz R&B Metal Dance Pop Indie & Lo-Fi

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18641024.pdfGenre Pop Rock Alternative Rock World Music Dance & Electronic Jazz R&B Metal Dance Pop Indie & Lo-Fi

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18704376.pdf · Train, CV and Test data HAAR Cascade to detect ace in the image+ resizing Gray scale conversion

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18704376.pdf · Train, CV and Test data HAAR Cascade to detect ace in the image+ resizing Gray scale conversion

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681727.pdf · Alex Fu, Yannick Meier, Elena Chen, Nithin Poduval 1. Introduction Depth prediction has been a problem

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681727.pdf · Alex Fu, Yannick Meier, Elena Chen, Nithin Poduval 1. Introduction Depth prediction has been a problem

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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

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/18681630.pdf · (Ng) "LSTM (long short term memory) unit" In the above formulas, the top equation of c represents

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681630.pdf · (Ng) "LSTM (long short term memory) unit" In the above formulas, the top equation of c represents

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Midterm Review - CS230 Deep Learningcs230.stanford.edu/fall2018/midterm_review.pdf · Midterm Review CS230 Fall 2018. Broadcasting. Calculating Means How would you calculate the means

Midterm Review - CS230 Deep Learningcs230.stanford.edu/fall2018/midterm_review.pdf · Midterm Review CS230 Fall 2018. Broadcasting. Calculating Means How would you calculate the means

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CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940460.pdf · also begun exploring deep unsupervised learning methods in the healthcare setting. One example includes

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940460.pdf · also begun exploring deep unsupervised learning methods in the healthcare setting. One example includes

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18673358.pdfThe ability to synthesize subsections of large volumes of texts into a concise, summarative format will

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18673358.pdfThe ability to synthesize subsections of large volumes of texts into a concise, summarative format will

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18679265.pdf · In the project, raw data and Founer transformed data are used as the inputs of the network. In the

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18679265.pdf · In the project, raw data and Founer transformed data are used as the inputs of the network. In the

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Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681213.pdf · The learning rate we choose is 0.00005 and batch ... Luke Metz, and Soumith Chintala. Unsupervised representation

Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681213.pdf · The learning rate we choose is 0.00005 and batch ... Luke Metz, and Soumith Chintala. Unsupervised representation

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

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

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$CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290433.pdf · Stanford University {zhaozhuo, zhiyuan8, edu Abstract Damage of building is an essential indicator$

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_spring_2019/reports/18681023.pdfFinally, we looked through a review paper on big data and tactical analysis in elite soccer [5]. This

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681023.pdfFinally, we looked through a review paper on big data and tactical analysis in elite soccer [5]. This

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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_spring_2019/reports/18681615.pdfStanford University 1050 Arastradero Rd., Stanford, CA kkaganov [ at ] stanford.edu Abstract In order

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CS230: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2019/cs230_lecture9.pdf · IV. Deep Q-Learning application: Breakout (Atari) Goal: play breakout, i.e. destroy

CS230: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2019/cs230_lecture9.pdf · IV. Deep Q-Learning application: Breakout (Atari) Goal: play breakout, i.e. destroy

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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18679149.pdf · U-Net is a popular network choice for image segmentation tasks. Its simple structure makes it easy

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18679149.pdf · U-Net is a popular network choice for image segmentation tasks. Its simple structure makes it easy

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Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited

Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited

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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

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: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2020/lecture9.pdfCS230: Lecture 9 Deep Reinforcement Learning Kian Katanforoosh Kian Katanforoosh I. Motivation

CS230: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2020/lecture9.pdfCS230: Lecture 9 Deep Reinforcement Learning Kian Katanforoosh Kian Katanforoosh I. Motivation

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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/15812441.pdfStackGAN managed to generate more realistic, higher resolution images by splitting the problem into two

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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

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 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18673372.pdfThe order of the summaries was randomized when read to decrease the effect of any sequential bias. Interestingly,

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18673372.pdfThe order of the summaries was randomized when read to decrease the effect of any sequential bias. Interestingly,

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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

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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