Understand What is a Generative Adversarial Networks, and their role in creating synthetic data. A Generative Adversarial Network (GAN) is a type of machine learning model composed of two neural networks: the generator and the discriminator. These networks work in tandem, with the generator creating synthetic data and the discriminator evaluating its authenticity compared to real data. Over time, the generator improves its outputs to produce realistic data indistinguishable from genuine samples. GANs are widely used in applications such as image generation, video synthesis, and creating lifelike animations. They also have applications in fields like healthcare for medical imaging and entertainment for developing realistic game graphics. GANs are a powerful tool in AI, showcasing how collaboration between networks can lead to innovative and creative solutions.