A learning-focused repository containing Python implementations of classical GAN architectures including DCGAN, Conditional GAN, Wasserstein GAN, and InfoGAN. All implementations use TensorFlow 1.0 with simple network structures and are tested on MNIST dataset. The repository includes utility modules for data preprocessing and network architectures, with generated samples saved to demonstrate training results.