Overfitting and Regularization For Deep Learning | Two Minute Papers #56

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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.

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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

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