# DL autoencoder
* Regularization
* Denoising
* Undercomplete Overcomplete
* Overcomplete case in which the hidden code hasdimension greater than the input
* An autoencoder whose code dimension is lessthan the input dimension is calledundercomplete
* Contractive autoencoder
Penalize 'unwanted variations'
Frobenius norm of Jacobian
* Variational Autoencoder
* How to generate samples from an auto encoder:
* Make hidden layer a distribution which is easy to sample from
# Generative Adversarial Networks
* Generate network
* Discriminator network
* Why is difficult to optimize
* How to evluate generative model