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Page 1: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,
Page 2: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,
Page 3: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,
Page 4: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,
Page 5: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,
Page 6: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,
Page 7: cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802593.pdf · 2019-04-04 · as Statoil, made data from the North Sea oil fields available for research in June,

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_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/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 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813120.pdf · animated images and applied to images earlier in the creative process. Style images from animated

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15342206.pdfNicole Kidman True Class Michael Jordan Michelle Obama Barack Obama Conclusion/Future Work 88 98 Identifying

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813480.pdf · 2019-04-04 · Yog.ai: Anna Lai alai2@stanf ord.edu Deep Learning for Yoga Bhargav Reddy brkreddy@stanf

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812470.pdf · upon them by pursuing deep learning techniques. Using techniques like LSTMs, RNNs, and highway networks,

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812222.pdf · 2019-04-04 · Iterative Cloud Point (ICP) with depth information or iterative model matching architecture

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

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_winter_2019/reports/15802990.pdf · 2019-04-04 · Both regression and classification approaches have been used to address issue of fake

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set

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_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/8290434.pdf · A Content-Based Image Retrieval System (CBIR) for eCommerce Purposes Using Deep Neural Networks Lee

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811511.pdf · (RNN, LSTM) to predict the direction of price movement (up or down) of the Dow Jones Industrial Average

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_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

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_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are

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.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_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,

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 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15871106.pdf5.0.2 Models Using the retrained Inception ResNet classifier, we ran several experiments to test the

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

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

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811878.pdf · striker (offensive agent) and goalie (defensive agent), we explore how agents can ... formation

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 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15808904.pdf · Erick Cardenas implemented the Convolutional Baseline model, setup and managed the AWS instances

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