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cs230.stanford.educs230.stanford.edu/projects_fall_2018/posters/12377987.pdf · U.S. Timely, accurate diagnosis is a critical factor in determining patient outcomes. Currently, pneumonia

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Page 1: cs230.stanford.educs230.stanford.edu/projects_fall_2018/posters/12377987.pdf · U.S. Timely, accurate diagnosis is a critical factor in determining patient outcomes. Currently, pneumonia

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

Documents
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

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

Documents
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

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_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/18679631.pdf · 2019-06-13 · train v2.csv - the updated training set - contains user transactions from August 1st

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18679631.pdf · 2019-06-13 · train v2.csv - the updated training set - contains user transactions from August 1st

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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

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/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.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285590.pdf · melody. Chord arrangement involves both conventional rules and creativity. Ideal model: Generate chords

cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285590.pdf · melody. Chord arrangement involves both conventional rules and creativity. Ideal model: Generate chords

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cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18662951.pdffrom the logs for different layers of the software stack, and abstract from a high level (later cnn

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18662951.pdffrom the logs for different layers of the software stack, and abstract from a high level (later cnn

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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.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

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_fall_2018/reports/12449174.pdf · YOLO ensembles performs marginally better than YOLO as a single model. In addition, some steps ofChexNet

cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12449174.pdf · YOLO ensembles performs marginally better than YOLO as a single model. In addition, some steps ofChexNet

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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8290434.pdf · A Content-Based Image Retrieval System (CBIR) for eCommerce Purposes Using Deep Neural Networks Lee

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8290434.pdf · A Content-Based Image Retrieval System (CBIR) for eCommerce Purposes Using Deep Neural Networks Lee

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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are

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CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12439806.pdfDefense of the Ancients (DOT A) 2 is a multiplayer online battle arena (MOBA) game developed by Valve Corporation

CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12439806.pdfDefense of the Ancients (DOT A) 2 is a multiplayer online battle arena (MOBA) game developed by Valve Corporation

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cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6940224.pdf · Exploring Knowledge Distillation of Deep Neural Networks for Efficient Hardware Solutions Haitong Li

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6940224.pdf · Exploring Knowledge Distillation of Deep Neural Networks for Efficient Hardware Solutions Haitong Li

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cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285130.pdf · Image Restoration of Noisy and Low-Quality Retinal Images Katherine Sytwul, Fariah Hayee2 Dept. of

cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285130.pdf · Image Restoration of Noisy and Low-Quality Retinal Images Katherine Sytwul, Fariah Hayee2 Dept. of

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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.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

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_winter_2019/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information

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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_fall_2018/reports/12447290.pdf · Emanuel Mendiola emanuelm@stanf ord. edu As techniques for creating photo realistic imagery evolve,

cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12447290.pdf · Emanuel Mendiola emanuelm@stanf ord. edu As techniques for creating photo realistic imagery evolve,

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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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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

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.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/18680300.pdfThe following equation gives the final probability density function (pdf) to predict the network output

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18680300.pdfThe following equation gives the final probability density function (pdf) to predict the network output

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cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8270111.pdf · With our efforts through this quarter, we have successfully built a speaker identification algorithm

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8270111.pdf · With our efforts through this quarter, we have successfully built a speaker identification algorithm

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CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12450133.pdf · using PERCEPTRON, ADALINE, MADALINE and BACK-PROPAGATION models. It turns out to work fairly well

CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12450133.pdf · using PERCEPTRON, ADALINE, MADALINE and BACK-PROPAGATION models. It turns out to work fairly well

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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)

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 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

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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