GAN stands for Generative Adversarial Networks. It is a type of machine learning technique that involves the use of two neural networks, namely, the generator and the discriminator, working together to create realistic data that appears to have come from a particular source. The generator is trained to create new data samples based on the input data that it has been fed, while the discriminator is trained to determine whether a given sample is real or fake. During training, both the generator and discriminator are updated iteratively based on their performance until the generator can create data that is very similar to the original data. GANs are typically used for generating images, but they can also be used for other types of data, such as music or speech. GANs have been used to create realistic images of faces, generate 3D models of objects, and even create entirely new art pieces. GANs have shown great potential in various fields like art, fashion, and gaming. The artic