Generative Models
Generative models are a class of machine learning models that are used to generate new data samples that are similar to the training data. These models learn the underlying distribution of the training data and can generate new samples by sampling from this distribution. Generative models can be used for a variety of tasks such as image generation, text generation, and speech synthesis. They are also used in unsupervised learning, where the goal is to learn the underlying structure of the data without any labels. Some popular generative models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Autoregressive models.
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