This lab will guide students through the process of training a Generative Adversarial Network (GAN) to generate image contents in DIGITS. After a quick review of the theory behind GANs we will train a GAN to generate images of handwritten digits using the MNIST dataset. We will create smooth animations of digits morphing across classes. In the second part of the lab we will apply these concepts on the CelebA dataset of celebrity faces. Using a pre-trained network we will see how to edit or retrieve face attributes (age, smile, etc.). We will see how to visualize the latent space in 3D and how to generate image analogies. Prerequisites: Prior experience of training neural networks. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.