• Home

  • Documents

  • cs230.stanford.educs230.stanford.edu/projects_spring_2019/posters/18655450.pdf · the album covers...
1

cs230.stanford.educs230.stanford.edu/projects_spring_2019/posters/18655450.pdf · the album covers as opposed to specific objects or items in those images (such as a guitar, etc.),

  • Upload
    others

  • View
    0

  • Download
    0

Embed Size (px)

Citation preview

Page 1: cs230.stanford.educs230.stanford.edu/projects_spring_2019/posters/18655450.pdf · the album covers as opposed to specific objects or items in those images (such as a guitar, etc.),

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

Documents
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

Documents
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

Documents
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

Documents
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

Documents
cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289547.pdf · MOOCs and online courses have notoriously high attrition [1]. One challenge is ... a student's performance

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289547.pdf · MOOCs and online courses have notoriously high attrition [1]. One challenge is ... a student's performance

Documents
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

Documents
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

Documents
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

Documents
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

Documents
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

Documents
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/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based

Documents
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

Documents
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

Documents
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

Documents
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

Documents
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

Documents
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

Documents
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

Documents
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

Documents
cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18664574.pdf · to identify the type combination of a Pokemon. Given an input of an RGB (3-channel) 64x64 Pokemon

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18664574.pdf · to identify the type combination of a Pokemon. Given an input of an RGB (3-channel) 64x64 Pokemon

Documents
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

Documents
cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811654.pdf · 2019-04-04 · Using preprocessing code provided by Kuleshov et al.'s GitHub repositoryl , I generated

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811654.pdf · 2019-04-04 · Using preprocessing code provided by Kuleshov et al.'s GitHub repositoryl , I generated

Documents
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

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_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,

Documents
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

Documents
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

Documents
cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813424.pdf · [1] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813424.pdf · [1] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks

Documents
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

Documents