CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled...

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Transcript of CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled...

Page 1: CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled ZSY. We used a TD- learning algorithm with hand-picked features that played many
Page 2: CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled ZSY. We used a TD- learning algorithm with hand-picked features that played many
Page 3: CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled ZSY. We used a TD- learning algorithm with hand-picked features that played many
Page 4: CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled ZSY. We used a TD- learning algorithm with hand-picked features that played many
Page 5: CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6940467.pdfCS 229 team that tackled ZSY. We used a TD- learning algorithm with hand-picked features that played many

web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6940392.pdf · observer and an object to facilitate vision-based obstacle perception [2]. Other remaining approaches

web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6940392.pdf · observer and an object to facilitate vision-based obstacle perception [2]. Other remaining approaches

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

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939740.pdf · Yelp review data, and report our generated sentences as being comparable to a traditional LSTM RNN.

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939740.pdf · Yelp review data, and report our generated sentences as being comparable to a traditional LSTM RNN.

Catalog of Clip in Hair from ZSY hair

Catalog of Clip in Hair from ZSY hair

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

Deep Learningcs230.stanford.edu/files_winter_2018/projects/6912923.pdf · reinforcement learning is used to address resource allocation and management. B. Voice Voice connection IS

Deep Learningcs230.stanford.edu/files_winter_2018/projects/6912923.pdf · reinforcement learning is used to address resource allocation and management. B. Voice Voice connection IS

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6908505.pdf · In this project, we build three deep learning models (DenseNet-121, DenseNet- LSTM and DenseNet-GRU)

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6908505.pdf · In this project, we build three deep learning models (DenseNet-121, DenseNet- LSTM and DenseNet-GRU)

Simplifying Grocery Checkout with Deep Learningcs230.stanford.edu/projects_fall_2019/reports/26257432.pdf · Simplifying Grocery Checkout with Deep Learning Jing Ning Department of

Simplifying Grocery Checkout with Deep Learningcs230.stanford.edu/projects_fall_2019/reports/26257432.pdf · Simplifying Grocery Checkout with Deep Learning Jing Ning Department of

web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6939387.pdf · product (e.g. women's jumpsuits). We collected the images by scraping seven shopping sites I

web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6939387.pdf · product (e.g. women's jumpsuits). We collected the images by scraping seven shopping sites I

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/posters/6880178.pdf · 2018-09-28 · Blackjack is one of the oldest casino games, and remains one of the most popular. In

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/posters/6880178.pdf · 2018-09-28 · Blackjack is one of the oldest casino games, and remains one of the most popular. In

Motivation Discussion Results - Deep Learningcs230.stanford.edu/projects_winter_2020/posters/32026369.pdf · SKIN CANCER SELF-DIAGNOSIS USING MOBILE DEVICE DERMATOSCOPIC ATTACHMENTS

Motivation Discussion Results - Deep Learningcs230.stanford.edu/projects_winter_2020/posters/32026369.pdf · SKIN CANCER SELF-DIAGNOSIS USING MOBILE DEVICE DERMATOSCOPIC ATTACHMENTS

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/files_winter_2018/projects/6940224.pdf · Exploring Knowledge Distillation of Deep Neural Networks for Efficient Hardware Solutions Haitong Li

web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6929846.pdf · Our dataset consists of approximately 400,000 image, label and caption triplets with 2600 unique

web.stanford.eduweb.stanford.edu/class/cs230/files_winter_2018/projects/6929846.pdf · Our dataset consists of approximately 400,000 image, label and caption triplets with 2600 unique

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

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/files_winter_2018/projects/6939642.pdf · with a non-native clip as the "content" and a US accent clip as the "style". The CNN classifier was

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939642.pdf · with a non-native clip as the "content" and a US accent clip as the "style". The CNN classifier was

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

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

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939620.pdf · Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6939620.pdf · Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the

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/12447633.pdfParas, Ledyba, Spinarak, Venonat, Sil- coon Lugia, Mesprit, Mew, Victini, Celebi, Cresselia, Volcanion,

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6922047.pdf · movie critic rating based on movie profiles using neural networks. In building our model we will use

CS230 Deep Learningcs230.stanford.edu/files_winter_2018/projects/6922047.pdf · movie critic rating based on movie profiles using neural networks. In building our model we will use