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Two Minute Papers: Training Deep Neural Networks With Dropout | Two Minute Papers #62

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 dropout.

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The paper “Dropout: A Simple Way to Prevent Neural Networks from
Overtting” is available here:
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf

Andrej Karpathy’s autoencoder is available here:
http://cs.stanford.edu/people/karpathy/convnetjs/demo/autoencoder.html

Recommended for you:
Overfitting and Regularization For Deep Learning – https://www.youtube.com/watch?v=6aF9sJrzxaM
Decision Trees and Boosting, XGBoost -https://www.youtube.com/watch?v=0Xc9LIb_HTw
A full playlist with machine learning and deep learning-related Two Minute Papers videos – https://www.youtube.com/playlist?list=PLujxSBD-JXglGL3ERdDOhthD3jTlfudC2

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