Use this course to learn at degree level how convolutional neural networks are used for visual recognition
Step-by-step guide
prereq CS229 Machine learning ML, formulating cost fn, taking derivatives,
performing optimisation w/ gradient descent- prereq CS109 basic probability and stats
- get PDF slides and video mpeg4 URLS from http://cs231n.stanford.edu/slides/2017/
- http://cs231n.github.io/ includes assignments
- demos http://vision.stanford.edu/teaching/cs231n-demos/linear-classify/
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you should know basic math : linear algebra, partial derivatives, integration, matrix multiplication etc It is helpful to learn "convolution and correlation for digital signal processing" to understand what the convolution math matrix realy do, |
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silvestro courses
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http://web.stanford.edu/class/cs231m/spring-2014/papers.html
http://web.stanford.edu/class/cs231a/course_notes.html
https://github.com/kenjihata/cs231a-notes
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