This report summarizes the NIPS 2016 tutorial on generative adversarial networks (GANs) by Ian Goodfellow. The tutorial covers why generative modeling is important, how generative models work, how GANs compare to other models, the mechanics of GANs, research frontiers, and state-of-the-art image models. It also includes three exercises and their solutions. The tutorial is not a comprehensive review of GANs but focuses on answering common questions and providing practical insights. Generative models, including GANs, are used for tasks like density estimation, sample generation, and multi-modal data modeling. GANs are particularly effective at generating samples, though they can also be used for density estimation. The tutorial explains that GANs are based on a game between a generator and a discriminator, where the generator tries to create realistic samples and the discriminator tries to distinguish real from fake samples. The generator and discriminator are trained simultaneously to minimize their respective costs. The tutorial also discusses the advantages and disadvantages of GANs compared to other models like variational autoencoders and Boltzmann machines. It highlights that GANs can generate samples in parallel, have fewer restrictions on the generator's design, and do not require Markov chains or variational bounds. However, training GANs requires finding the Nash equilibrium of a game, which is more challenging than optimizing an objective function. The tutorial also covers different cost functions used in GANs, including the discriminator's cost and the generator's cost, and discusses the theoretical foundations of GANs, such as the minimax game and the maximum likelihood game. The tutorial concludes that GANs are effective for generating realistic samples and have many applications in image generation, super-resolution, and image-to-image translation.This report summarizes the NIPS 2016 tutorial on generative adversarial networks (GANs) by Ian Goodfellow. The tutorial covers why generative modeling is important, how generative models work, how GANs compare to other models, the mechanics of GANs, research frontiers, and state-of-the-art image models. It also includes three exercises and their solutions. The tutorial is not a comprehensive review of GANs but focuses on answering common questions and providing practical insights. Generative models, including GANs, are used for tasks like density estimation, sample generation, and multi-modal data modeling. GANs are particularly effective at generating samples, though they can also be used for density estimation. The tutorial explains that GANs are based on a game between a generator and a discriminator, where the generator tries to create realistic samples and the discriminator tries to distinguish real from fake samples. The generator and discriminator are trained simultaneously to minimize their respective costs. The tutorial also discusses the advantages and disadvantages of GANs compared to other models like variational autoencoders and Boltzmann machines. It highlights that GANs can generate samples in parallel, have fewer restrictions on the generator's design, and do not require Markov chains or variational bounds. However, training GANs requires finding the Nash equilibrium of a game, which is more challenging than optimizing an objective function. The tutorial also covers different cost functions used in GANs, including the discriminator's cost and the generator's cost, and discusses the theoretical foundations of GANs, such as the minimax game and the maximum likelihood game. The tutorial concludes that GANs are effective for generating realistic samples and have many applications in image generation, super-resolution, and image-to-image translation.