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Page 1: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based
Page 2: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based
Page 3: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based
Page 4: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based
Page 5: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based
Page 6: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based
Page 7: cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based

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_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_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_spring_2018/reports/8289231.pdf · high gesture classification accuracy can be achieved using a convolutional neural network trained

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8289231.pdf · high gesture classification accuracy can be achieved using a convolutional neural network trained

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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.edu › projects_spring_2018 › reports › 8291236… · Pillow, pytest, h5py, sklearn, scipy, scikit-image, scikit-learn, keras [7, 10, 5] 5 Results, Metrics, and

cs230.stanford.edu › projects_spring_2018 › reports › 8291236… · Pillow, pytest, h5py, sklearn, scipy, scikit-image, scikit-learn, keras [7, 10, 5] 5 Results, Metrics, and

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8271110.pdf · 2018. 9. 28. · reconstruction using e.g. template fitting. None of these methods are fully satisfactory

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8271110.pdf · 2018. 9. 28. · reconstruction using e.g. template fitting. None of these methods are fully satisfactory

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cs230.stanford.educs230.stanford.edu › projects_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

cs230.stanford.educs230.stanford.edu › projects_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

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web.stanford.eduweb.stanford.edu/class/cs230/projects_spring_2018/... · 2018-09-28 · generator, 2) predictive algorithms, 3) portfolio design and risk management parameters, and

web.stanford.eduweb.stanford.edu/class/cs230/projects_spring_2018/... · 2018-09-28 · generator, 2) predictive algorithms, 3) portfolio design and risk management parameters, and

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CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290890.pdfFigure 1: Next Pitch Type Prediction Problem Unlike other sports, a baseball game can be described well

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8290890.pdfFigure 1: Next Pitch Type Prediction Problem Unlike other sports, a baseball game can be described well

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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 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8285485.pdf · For our project we use the following data set the "Coupon Purchase Prediction" challenge from the

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/reports/8285485.pdf · For our project we use the following data set the "Coupon Purchase Prediction" challenge from the

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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.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_2018/reports/8288946.pdf · jazz piano piece). It was converted into a text file, which contains its noteOn, noteOff, control

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8288946.pdf · jazz piano piece). It was converted into a text file, which contains its noteOn, noteOff, control

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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.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289789.pdfThe final number of nodes in order from the first layer to the softmax layer was eight, six and five, respectively

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289789.pdfThe final number of nodes in order from the first layer to the softmax layer was eight, six and five, respectively

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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_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

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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_fall_2018/reports/12449630.pdf · OpenAI Gym's classic control tasks are less explored. This study aims to present and compare results

cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12449630.pdf · OpenAI Gym's classic control tasks are less explored. This study aims to present and compare results

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cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813002.pdf · global education equity (4). CS230: Deep Learning, Winter 2019, Stanford ... To evolve beyond our

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813002.pdf · global education equity (4). CS230: Deep Learning, Winter 2019, Stanford ... To evolve beyond our

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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.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

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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/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_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_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_fall_2018/posters/12377987.pdf · U.S. Timely, accurate diagnosis is a critical factor in determining patient outcomes. Currently, pneumonia

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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 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.stanford.educs230.stanford.edu/projects_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

cs230.stanford.educs230.stanford.edu/projects_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

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