This repository collects documented tricks and best practices for training Generative Adversarial Networks, originally presented as a starter resource at NIPS 2016. It covers input normalization, loss function modifications, latent space sampling strategies, batch normalization approaches, activation function choices, and other empirically validated training techniques. The content is crowd-sourced via pull requests from practitioners who have found particular methods effective in practice.