FROM SCRATCH - GENERATIVE ADVERSARIAL NETWORKS

Summary from the TLDR Web Dev newsletter

Generative Adversarial Networks (GANs) are a generative AI method consisting of two models: a generator and a discriminator. The generator tries to create realistic samples from a training data distribution, while the discriminator aims to distinguish between real and generated samples. The two models are trained in a competitive process, with the generator improving its ability to generate realistic samples and the discriminator enhancing its ability to differentiate them. This article goes through building GANs from scratch.