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Unleashing the Power of Conditional GANs: A Hands-On Guide to Generating Realistic Tabular Data
A detailed tutorial on implementing Conditional Generative Adversarial Networks (CGANs) for generating synthetic tabular data, demonstrated through a practical example using the Adult Income Dataset.
The implementation covers everything from data preprocessing to model architecture, training, and synthetic data generation with specific conditions.

Reasons to Read -- Learn:

  • how to implement a complete CGAN system in PyTorch, with practical code examples for generating synthetic tabular data conditioned on specific attributes like education level
  • key differences between traditional GANs and CGANs, including how conditional information is incorporated into both generator and discriminator networks to control the data generation process
  • detailed data preprocessing techniques for handling mixed categorical and numerical data, including normalization, encoding, and how to transform synthetic data back to its original form
  • 9 min readauthor: Harish Siva Subramanian
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