Generative Adversarial Networks are a type of Machine Learning model that consists of two neural networks: a generator and a discriminator. GANs are used to generate synthetic data that resembles real data by learning from training examples. They find applications in image generation, video synthesis, and data augmentation.
Unsupervised Learning is a Machine Learning technique where models learn patterns and structures within data without labelled examples. By uncovering hidden relationships and clustering…