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# System prepended metadata

title: DL autoencoder

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# 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 