Generative Adversarial Networks

A Generative Adversarial Network (GAN) trains two neural networks in opposition: a Generator (G) that maps latent noise (optionally conditioned on input) into synthetic samples, and a Discriminator (D) that distinguishes real data from generated data and provides gradients that drive G to improve; training is a minimax game where D maximizes correct real/fake classification while G minimizes D’s ability to detect fakes, producing a model that can sample from a learned data distribution without explicit likelihood modeling.

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This repo contains notebook implementations of multiple GAN families and a production-grade system design that describes how to operationalize generative models end-to-end (versioned data → training → evaluation gates → deployment → monitoring → retraining).

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