Autoencoders as a dimensionality reduction algorithm

[jeremymanning]

We could support dimensionality reduction through autoencoders. Here's a useful looking tutorial (it looks relatively straightforward to implement all of the variants in the tutorial): https://blog.keras.io/building-autoencoders-in-keras.html

• [ ] shallow autoencoder
• [ ] deep autoencoder
• [ ] sparse autoencoder
• [ ] convolutional autoencoder
• [ ] sequence-to-sequence autoencoder (good for timeseries data)
• [ ] variational autoencoder

[jeremymanning]

If we implement this using the standard hypertools API (e.g. through the reduce function/flag), it would provide a really convenient way to train and apply autoencoders. I think some neat demos would come out of it.