A Practical Guide to Generating New Data with Variational Autoencoders in PyTorch

What You Will Learn from This Article

• 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.