* Beta VAE connected to a normalizing flow of selection
* Noisy moons
*initial test data - [see usage](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)
*[Initial test data](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)
* Thoracic surgery
*current test data - [find out more](https://www.kaggle.com/sid321axn/thoraric-surgery)
*[Current test data](https://www.kaggle.com/sid321axn/thoraric-surgery)
* Beta-VAE
*variational autoencoder algorithm extended with a beta parameter to put implicit pressure on the learnt posterior
*[find out more](https://paperswithcode.com/method/beta-vae)
*Variational autoencoder algorithm extended with a beta parameter to put implicit pressure on the learnt posterior
*[Find out more](https://paperswithcode.com/method/beta-vae)
### Updates
*[Preprocessing](https://github.com/kaanguney/normalizing_flows/tree/main/scripts/preprocessing) currently supports a dataset called `prostate.xls`. Now supports `ThoracicSurgery.csv` as well.
