- Gain a basic understanding of the theory behind Variational Autoencoders (VAEs).
- Learn how to implement a VAE in PyTorch using the Wine dataset.
- Discover how to combine PCA with VAEs for dimensionality reduction and visualization.
- Learn how to apply a classification method to predict labels for the data generated by the VAE.
- Statistical metrics used for evaluation.
- Weaknesses of VAEs for Tabular Data
- Tab-VAE: A Novel VAE for Synthetic Tabular Data.