Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07
Highlights:
Occam's razor
Weight decay
L2 regularization / Ridge regression
L1 regularization / LASSO
Sparsity
Early stopping
Dropout
Ensemble methods
Parameter sharing
Data augmentation
Further reading:
Deep Learning by Ian Goodfellow:
http://www.deeplearningbook.org/
Introduction to Machine Learning by Ethem Alpaydin
https://www.cmpe.boun.edu.tr/~ethem/i2ml2e/
CS231n: Convolutional Neural Networks for Visual Recognition
http://cs231n.github.io/
Dropout: a simple way to prevent neural networks from overfitting
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
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