Generative models are a type of machine learning algorithm designed to create new data samples that resemble a given dataset. They learn the underlying patterns and structures of the input data, allowing them to generate new instances that share similar characteristics. Common applications include image generation, text creation, and music composition. One popular example of a generative model is the Generative Adversarial Network (GAN), which consists of two neural networks—the generator and the discriminator—working against each other. The generator creates new data, while the discriminator evaluates its authenticity, leading to improved outputs over time.