Generative models are a type of machine learning model that are designed to generate new data that is similar to existing data. The goal of a generative model is to learn the underlying structure and patterns in a dataset, and then use this knowledge to create new data that is similar to what it has seen before.

Generative models can be used for a variety of purposes, including:

1. Image generation: Generating new images based on existing images, such as generating new faces based on a dataset of face images.
2. Text generation: Generating new text based on existing text, such as generating new headlines based on a dataset of news articles.
3. Music generation: Generating new music based on existing music, such as generating new songs based on a dataset of MIDI files.
4. Video generation: Generating new videos based on existing videos, such as generating new dance sequences based on a dataset of dance videos.

Generative models can be implemented using a variety of techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs). These models typically involve training on large datasets and using techniques such as stochastic gradient descent to optimize the model parameters.

Generative models are an active area of research and development, and they are expected to play an increasingly important role in various applications, such as computer graphics, computer vision, natural language processing, and robotics. The goal of generative models is to enable machines to generate new data in a way that is as creative and diverse as possible, leading to new and innovative applications in a wide range of fields.