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Page 1: 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

Page 2: 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

Page 3: 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

Page 4: 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

Page 5: 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

Page 6: 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

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812605.pdf · FMA( free music archive) . The GTZAN dataset consists of 1000 audio tracks each 30 seconds long

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/15812222.pdf · 2019-04-04 · Iterative Cloud Point (ICP) with depth information or iterative model matching architecture

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/15812313.pdf · As you can see, Trump. In this case, our mask will be an array of ai this mask turns off vectors

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15791197.pdf3.1 Manga The manga dataset is a subset of Manga109 [9], [8]. Manga109 consists of manga pages from 109

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_winter_2019/reports/15813329.pdf · from a 2019 Kaggle Competition*. The latest model achieved 97.2% accuracy against the test set

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813172.pdf80/10/10 train, dev, and test splits based on recom- Fig. 1) The Starry Night by Vincent van Gogh [1]

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information

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/15806104.pdf · Description 3 inputs, 1 hidden layer, 100 units 3 inputs, 1 hidden layer, 100 units 4 inputs, 1

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

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15773386.pdf · camera at any given position and orientation. A random sampling of camera positions is taken within

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812659.pdf · 2019. 4. 4. · mean lower IOU for the YOLO model in many cases). We have pre-processed these images

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15795079.pdf · 2019. 4. 4. · re n [1] Hingmire, S.; Chougule, S., Palshikar, G. K., and Chakraborti, S. 2013

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813468.pdf · training a machine learning model. For the audio classifier presented in this work, the inputs to the

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_winter_2019/reports/15813424.pdf · [1] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks

CS230 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15812583.pdfwith 1,716 car models. The full car images are labeled with bounding boxes and viewpoints. Each car model

Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15810323.pdfNetworks and Siamese Neural Networks Marios Andreas Galanis, Vladimir Kozlow ... There is a very large body

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 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_winter_2019/reports/15809694.pdfexisting previous piece of artwork in a personalized manner. In our method, we alter an existing piece

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

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

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/15792433.pdf · supermarkets: four Safeways near Palo Alto, SF and one H-E-B in Austin, TX. Between 40 and 60 snapshots

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813002.pdf · global education equity (4). CS230: Deep Learning, Winter 2019, Stanford ... To evolve beyond our

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

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 Deep Learningcs230.stanford.edu/projects_winter_2019/reports/15813450.pdfInput (current flight) LSTM seq Dense (x5) Prediction Figure 2: LSTM + CNN Architecture Due to the class

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_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/15813327.pdf0.024 0.022 0.020 Train 0.029 0.036 0.051 Test 0.048 0.026 0.028 Train 81.19% 87.55% 74.60% Test 85.82%