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Two Minute Papers · 发布于 2016年3月30日
In this episode, we discuss the bane of many machine learning algorithms - overfitting. It is also explained why it is an undesirable way to learn and how to combat it via L1 and L2 regularization. _____________________________ The paper "Regression Shrinkage and Selection via the Lasso" is available here: http://statweb.stanford.edu/~tibs/lasso/lasso.pdf Andrej Karpathy's excellent lecture notes on neural networks and regularization: http://cs231n.github.io/neural-networks-1/ The neural network demo is available here: http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html A playlist with out neural network and deep learning-related videos: https://www.youtube.com/playlist?list=PLujxSBD-JXglGL3ERdDOhthD3jTlfudC2 WE WOULD LIKE TO THANK OUR GENEROUS SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Vinay S. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_center?add_user=keeroyz The thumbnail image background was created by Tony Hisgett (CC BY 2.0). It has undergone recoloring. - https://flic.kr/p/5dkbNV Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Patreon → https://www.patreon.com/TwoMinutePapers Facebook → https://www.facebook.com/TwoMinutePapers/ Twitter → https://twitter.com/karoly_zsolnai Web → https://cg.tuwien.ac.at/~zsolnai/