Stanford University CS231n: Convolutional Neural Networks for Visual Recognition
The Convolutional Neural Network in this example is classifying images live in your browser using Javascript, at about 10 milliseconds per image. It takes an input image and transforms it through a series of functions into class probabilities at the end. The transformed representations in this visualization can be losely thought of as the activations of the neurons along the way. The parameters of
Index of /slides/2017
Index of /slides/2017 NameLast modifiedSizeDescription Parent Directory - cs231n_2017_lecture1.pdf2017-04-04 23:27 16M cs231n_2017_lecture2.pdf2017-04-06 12:14 5.3M cs231n_2017_lecture3.pdf2017-04-11 14:05 4.6M cs231n_2017_lecture4.pdf2017-04-14 20:15 2.6M cs231n_2017_lecture5.pdf2017-07-05 16:41 10M cs231n_2017_lecture6.pdf2017-04-20 11:02 3.0M cs231n_2017_lecture7.pdf2017-04-26 00:07 2.7M cs231
Lecture 9 - 3 Feb 2016Fei-Fei Li & Andrej Karpathy & Justin JohnsonFei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 9 - 3 Feb 20161 Lecture 9: Understanding and Visualizing Convolutional Neural Networks Lecture 9 - 3 Feb 2016Fei-Fei Li & Andrej Karp
Lecture 9 - 3 Feb 2016Fei-Fei Li & Andrej Karpathy & Justin JohnsonFei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 9 - 3 Feb 20161 Lecture 9: Understanding and Visualizing Convolutional Neural Networks Lecture 9 - 3 Feb 2016Fei-Fei Li & Andrej Karpathy & Justin JohnsonFei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 9 - 3 Feb 20162 Administrative - A1 is graded. We’ll send out grades t
http://cs231n.stanford.edu/reports2016/259_Report.pdf
Multiple Object Recognition with Focusing and Blurring Holly Chiang Stanford University hchiang1@stanford.edu Yifan Ge Stanford University gyifan@stanford.edu Connie Wu Stanford University wuconnie@stanford.edu Abstract In this project we use CNNs to identify significant objects in a scene for applications in photo or video editing. We tackle this problem with two different approaches: faster R- C
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https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf
https://cs231n.stanford.edu/reports/2017/pdfs/19.pdf
https://cs231n.stanford.edu/reports/2017/pdfs/203.pdf
https://cs231n.stanford.edu/reports/2017/pdfs/209.pdf
https://cs231n.stanford.edu/reports/2017/pdfs/513.pdf
https://cs231n.stanford.edu/reports/2017/pdfs/614.pdf
https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf
https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf
https://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture13.pdf
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture4.pdf
http://cs231n.stanford.edu/handouts/derivatives.pdf
http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf
http://cs231n.stanford.edu/reports/2016/pdfs/286_Report.pdf
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf
https://cs231n.stanford.edu/reports/2016/pdfs/262_Report.pdf