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Page 1: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

Page 2: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

Page 3: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

Page 4: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

Page 5: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

Page 6: CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447287.pdf · Similarity and tf-idf are insufficient for questions but are more effective for bags of words approaches

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15811843.pdfincluding flipping, cropping, rotating, and etc. MOM Figure 1. Sample image of Bart Simpson, Homer Simpson

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_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_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_winter_2019/reports/15806293.pdf · Most sentiment analysis studies in the finance and accounting literature use ... Apple Inc. : ]

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational

Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12418781.pdf · 2019-01-16 · Figure 1: Prevalence of Different Road Objects in the BDD-IOOK Dataset. In order to implement

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 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6933119.pdf2 Methods: Quantum mechanics as an optimization problem Carleo [2017] outlines a theoretical formulation

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_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_spring_2018/reports/8290634.pdf · alternative of rating food photos' attractiveness to Yelp's published approach that utilized EXIF

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/12449264.pdfadversarial network (GAN) based off of the SRGAN network developed by Ledig et. al [2]. We modified this

cs230.stanford.edu › projects_fall_2018 › reports › ... · 2019-01-16 · 2 Related Work Many work has been ... From the DCGAN loss training curve in Fig.5, we can see that

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.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_winter_2019/reports/15810428.pdf · Food image classification has many uses in everyday tasks, ranging from search to tagging. In our

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 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12437786.pdf · ANET achieved 0.87 recall rate across all test cases. CS230: Deep Learning, Fall 2018, Stanford University,

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_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_spring_2018/reports/8290433.pdf · Stanford University {zhaozhuo, zhiyuan8, edu Abstract Damage of building is an essential indicator

CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12447633.pdfParas, Ledyba, Spinarak, Venonat, Sil- coon Lugia, Mesprit, Mew, Victini, Celebi, Cresselia, Volcanion,

CS230 Deep Learningcs230.stanford.edu/projects_fall_2018/reports/12450134.pdf · Multi-object detection is the task of finding objects in an image or video frame, which consists of

CS230: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2020/lecture9.pdfCS230: Lecture 9 Deep Reinforcement Learning Kian Katanforoosh Kian Katanforoosh I. Motivation

CS230 Deep Learningcs230.stanford.edu/projects_spring_2018/posters/8284325.pdf · 2018. 9. 28. · Aditya Chander ( [email protected] ), Marina Cottrell ( [email protected] ),

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/15342206.pdfNicole Kidman True Class Michael Jordan Michelle Obama Barack Obama Conclusion/Future Work 88 98 Identifying

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_2018/posters/8285188.pdfExplore and develop a deep machine learning model that predicts the future price of digital asset such

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/files_winter_2018/projects/6940506.pdf · OCR focused on historical transcription has been rarely applied on Arabic historical manuscripts

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

Midterm Review - CS230 Deep Learningcs230.stanford.edu/fall2018/midterm_review.pdf · Midterm Review CS230 Fall 2018. Broadcasting. Calculating Means How would you calculate the means