cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681243.pdf · connected layers...

6

Transcript of cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681243.pdf · connected layers...

Page 1: 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
Page 2: 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
Page 3: 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
Page 4: 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
Page 5: 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
Page 6: 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_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

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

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,

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

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

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

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,

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

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

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

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

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

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

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

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

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811350.pdf · Monet painting to photo task. We gather Monet paintings both from the internet and from Wikiart.org.

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811350.pdf · Monet painting to photo task. We gather Monet paintings both from the internet and from Wikiart.org.

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

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

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

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