Microsoft Word - ChaiRameshYeo-AreAnimeCartoons-report.docx
1 Abstract The realm of popular culture is full of debate: Are ninjas or pirates better? Is the Flash faster than Superman? Which character pairing is the best? This project targets the age-old debate: Are anime cartoons? More specifically, we wish to see if a machine can learn to differentiate between anime and cartoon. For this project, we collected 3600 screenshots of popular cartoons and anime
Packbot-P Summary Design and SOW
Andrew Y. Ng Advice for applying Machine Learning Andrew Ng Stanford University Andrew Y. Ng Today’s Lecture • Advice on how getting learning algorithms to different applications. • Most of today’s material is not very mathematical. But it’s also some of the hardest material in this class to understand. • Some of what I’ll say today is debatable. • Some of what I’ll say is not good advice for doin
Automatic Segmentation of Clothing for the Identification of Fashion Trends Using K-Means Clustering
CS 229 Machine Learning Final Projects, Autumn 2015
CS 229 Machine Learning Final Projects, Autumn 2015 Navigation Best Project Awards Athletics & Sensing Devices Audio & Music Computer Vision Finance & Commerce General Machine Learning Life Sciences Natural Language Physical Sciences Theory & Reinforcement Best Project Awards Public Vote Your Next Personal Trainer [poster] [report] Brandon Garcia, Computer Science Russell Kaplan, Computer Science
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Linear Algebra Review and Reference Zico Kolter (updated by Chuong Do) September 30, 2015 Contents 1 Basic Concepts and Notation 2 1.1 Basic Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Matrix Multiplication 3 2.1 Vector-Vector Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Matrix-Vector Products . . . . . . . . . . . . . . . . . . .
CS 229: Machine Learning Final Projects, Autumn 2014
CS 229 Machine Learning Final Projects, Autumn 2014 Nonlinear Reconstruction of Genetic Networks Implicated in AML.Aaron Goebel, Mihir Mongia .[pdf] Can Machines Learn Genres.Aaron Kravitz, Eliza Lupone, Ryan Diaz.[pdf] Identifying Gender From Facial Features.Abhimanyu Bannerjee, Asha Chigurupati.[pdf] Equation to LaTeX.Abhinav Rastogi, Sevy Harris.[pdf] Intensity prediction using DYFI.Abhineet Gu
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https://cs229.stanford.edu/proj2017/final-reports/5244110.pdf
https://cs229.stanford.edu/proj2013/Hong-GaussianProcessedBasedImageSegmentationAndObjectDetection.pdf
http://cs229.stanford.edu/notes/cs229-notes3.pdf
https://cs229.stanford.edu/proj2013/ZhouMashuq-WebContentExtractionThroughMachineLearning.pdf
Predicting airline delays
https://cs229.stanford.edu/proj2015/218_report.pdf
http://cs229.stanford.edu/notes/cs229-notes1.pdf
https://cs229.stanford.edu/proj2015/054_report.pdf
https://cs229.stanford.edu/proj2012/ChinchaliTandon-DeepLearningforWirelessInterferenceSegmentationandPrediction.pdf
http://cs229.stanford.edu/notes/cs229-notes2.pdf
https://cs229.stanford.edu/proj2014/David%20Montague,%20Algorithmic%20Trading%20of%20Futures%20via%20Machine%20Learning.pdf
https://cs229.stanford.edu/proj2014/Christopher%20Aberger,%20Recommender.pdf