A Generative Adversarial Network (GAN) is a zero-sum game of unsupervised machine learning in which a discriminator and a generator compete against each other. The generator generates new synthetic data and a discriminator evaluates the data for authenticity. In this case, a GAN was trained over 200 epochs using the MNSIT handwritten digits database: https://keras.io/datasets
