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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

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Page 1: 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
Page 2: 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
Page 3: 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
Page 4: 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
Page 5: 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
Page 6: 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.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 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.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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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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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 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/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18681176.pdfreviews, whether they are movie reviews, Amazon reviews, workplace reviews is a common occurrence in

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18681176.pdfreviews, whether they are movie reviews, Amazon reviews, workplace reviews is a common occurrence in

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674572.pdf · In this study, predictive multi-class models are trained for chest x-ray diagnosis of 14 observations

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674572.pdf · In this study, predictive multi-class models are trained for chest x-ray diagnosis of 14 observations

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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.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 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 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290634.pdf · alternative of rating food photos' attractiveness to Yelp's published approach that utilized EXIF

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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_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 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678885.pdfis a treasure trove of information, including that related to virality, user sentiment, networks, and

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678885.pdfis a treasure trove of information, including that related to virality, user sentiment, networks, and

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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_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/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational

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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 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/files_winter_2018/projects/6933119.pdf2 Methods: Quantum mechanics as an optimization problem Carleo [2017] outlines a theoretical formulation

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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 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680161.pdf · separation (BSS) eval in particular, source signal-to-distortion ratio (SDR) and signal-to-interference

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680161.pdf · separation (BSS) eval in particular, source signal-to-distortion ratio (SDR) and signal-to-interference

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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

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/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing

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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_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/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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CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674654.pdf · Not only is there risk of a valuation being biased due to the prejudices of an individual, but the

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674654.pdf · Not only is there risk of a valuation being biased due to the prejudices of an individual, but the

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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

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 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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