In this episode, we dive into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised!

VAE's are a very hot topic right now in unsupervised modelling of latent variables and provide a unique solution to the curse of dimensionality.

This video starts with a quick intro into normal autoencoders and then goes into VAE's and disentangled beta-VAE's.
I aslo touch upon related topics like learning causal, latent representations, image segmentation and the reparameterization trick!

Get ready for a pretty technical episode!

Paper references:
- Disentangled VAE's (DeepMind 2016): https://arxiv.org/abs/1606.05579
- Applying disentangled VAE's to RL: DARLA (DeepMind 2017): https://arxiv.org/abs/1707.08475
- Original VAE paper (2013): https://arxiv.org/abs/1312.6114

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