Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we...

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Transcript of Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we...

Page 1: Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited.
Page 2: Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited.
Page 3: Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited.
Page 4: Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited.
Page 5: Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited.
Page 6: Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681694.pdf · means even if we implemented a perfect predictor for question spans, the maximum improvement is very limited.

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 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/15811878.pdf · striker (offensive agent) and goalie (defensive agent), we explore how agents can ... formation

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681331.pdf · for a specific digit in a "hand written digit recognition problem". This may lead to an inaccurate

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681331.pdf · for a specific digit in a "hand written digit recognition problem". This may lead to an inaccurate

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

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

Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681213.pdf · The learning rate we choose is 0.00005 and batch ... Luke Metz, and Soumith Chintala. Unsupervised representation

Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681213.pdf · The learning rate we choose is 0.00005 and batch ... Luke Metz, and Soumith Chintala. Unsupervised representation

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_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_spring_2019/reports/18674654.pdf · Not only is there risk of a valuation being biased due to the prejudices of an individual, but the

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674654.pdf · Not only is there risk of a valuation being biased due to the prejudices of an individual, but the

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_2018/reports/8290634.pdf · alternative of rating food photos' attractiveness to Yelp's published approach that utilized EXIF

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/15782825.pdf · Generative Adversarial Networks (GANs) [Goodfellow et al, 2014; Isola et al, 2017] and Variational

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18679304.pdfIn order to infer spatial location through neural data over time, we have adapted an Long Short Term

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18679304.pdfIn order to infer spatial location through neural data over time, we have adapted an Long Short Term

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_2019/reports/18681189.pdfMore broadly our project is part of the growing field of object detection and classification. A future

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: 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 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681023.pdfFinally, we looked through a review paper on big data and tactical analysis in elite soccer [5]. This

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681023.pdfFinally, we looked through a review paper on big data and tactical analysis in elite soccer [5]. This

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674572.pdf · In this study, predictive multi-class models are trained for chest x-ray diagnosis of 14 observations

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18674572.pdf · In this study, predictive multi-class models are trained for chest x-ray diagnosis of 14 observations

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680476.pdfval acc no dropout val acc dropout—OS val acc dropout—OS 12—0.01 (a) Accuracy Figure 5: Customized

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680476.pdfval acc no dropout val acc dropout—OS val acc dropout—OS 12—0.01 (a) Accuracy Figure 5: Customized

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute prob- lem stemmmg from

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678420.pdf · In large developing countries such as India and China, there exists an acute prob- lem stemmmg from

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_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_spring_2018/reports/8289231.pdf · high gesture classification accuracy can be achieved using a convolutional neural network trained

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