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Page 1: cs230.stanford.edu · The dataset was obtained through the Kern Scores library at the Center Computer Assisted Research in the Humanities at Stanford University[l()]. Kern contains
Page 2: cs230.stanford.edu · The dataset was obtained through the Kern Scores library at the Center Computer Assisted Research in the Humanities at Stanford University[l()]. Kern contains
Page 3: cs230.stanford.edu · The dataset was obtained through the Kern Scores library at the Center Computer Assisted Research in the Humanities at Stanford University[l()]. Kern contains
Page 4: cs230.stanford.edu · The dataset was obtained through the Kern Scores library at the Center Computer Assisted Research in the Humanities at Stanford University[l()]. Kern contains
Page 5: cs230.stanford.edu · The dataset was obtained through the Kern Scores library at the Center Computer Assisted Research in the Humanities at Stanford University[l()]. Kern contains
Page 6: cs230.stanford.edu · The dataset was obtained through the Kern Scores library at the Center Computer Assisted Research in the Humanities at Stanford University[l()]. Kern contains

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_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

CS230: Lecture 5cs230.stanford.edu/fall2017/files/CS230_Handout6.pdf · CS230: Lecture 5 Advanced topics in Object Detection Kian Katanforoosh, Andrew Ng Stanford University. Kian

CS230 Final Project Poster Jeongseo - Compatibility Modecs230.stanford.edu/projects_fall_2019/posters/26261075.pdf · Title: Microsoft PowerPoint - CS230_Final_Project_Poster_Jeongseo

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813380.pdf · CS230 Final Project: Milestone Topic: Transfer Learning Ajay Sohmshetty (collaboration with Amir

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

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/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_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

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

Generating Probability Distributions for Future Stock …cs230.stanford.edu/projects_winter_2020/reports/32144605.pdfGenerating Probability Distributions for Future Stock Prices Jeffrey

cs230.stanford.edu · making geologically reasonable and consistent predictions for seismic facies. 1 Introduction The increasing availability of three-dimensional (3D) seismic data

CS230-Fall 2019 Final Project Report Painting Genre ...cs230.stanford.edu/projects_fall_2019/reports/26180781.pdf · CS230-Fall 2019 Final Project Report Painting Genre Classification

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: Lecture 3cs230.stanford.edu/files/lecture-notes-3.pdf · CS230: Lecture 3 The mathematics of deep learning Backpropagation, Initializations, Regularization Kian Katanforoosh

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

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_spring_2019/posters/18681176.pdfreviews, whether they are movie reviews, Amazon reviews, workplace reviews is a common occurrence in

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: Lecture 9 Deep Reinforcement Learningcs230.stanford.edu/spring2019/cs230_lecture9.pdf · IV. Deep Q-Learning application: Breakout (Atari) Goal: play breakout, i.e. destroy

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

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

Real-Time Seismic Attributes Computation with Conditional …cs230.stanford.edu/projects_winter_2020/reports/32623969.pdfIn this context, seismic attributes contribute to the process,

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

White Christmas: Remaking Augmented Reality Censorship ...cs230.stanford.edu/projects_fall_2019/reports/26262130.pdf · MOT 2016 dataset and trained the instance segmentation part

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] ),