cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In...

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Transcript of cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In...

Page 1: cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In this paper, Feng compares deep vs shallow networks on the music genre classification
Page 2: cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In this paper, Feng compares deep vs shallow networks on the music genre classification
Page 3: cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In this paper, Feng compares deep vs shallow networks on the music genre classification
Page 4: cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In this paper, Feng compares deep vs shallow networks on the music genre classification
Page 5: cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In this paper, Feng compares deep vs shallow networks on the music genre classification
Page 6: cs230.stanford.edu · The first point of reference for this project was Tao Feng's work (2014) . In this paper, Feng compares deep vs shallow networks on the music genre classification

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/reports/15811869.pdf · realistic personalised letters, formulating digital signatures, etc. In order to preserve information

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

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

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

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.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/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.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/posters/8285590.pdf · melody. Chord arrangement involves both conventional rules and creativity. Ideal model: Generate chords

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: 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 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.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

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

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/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.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802276.pdf · each artist. The resulting model attained good performance over the baseline, and provided subjectively

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802276.pdf · each artist. The resulting model attained good performance over the baseline, and provided subjectively

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.stanford.educs230.stanford.edu/projects_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

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

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

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.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

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

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

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