Generative Models
Generative models are a class of machine learning models designed to learn the underlying probability distribution of a dataset and generate new data samples that resemble the original data. They are widely used in tasks such as image synthesis, text generation, and data augmentation. Key types include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models like GPT.
Developers should learn generative models for applications in creative AI, such as generating realistic images, videos, or text, and for data enhancement in scenarios with limited training data. They are essential in fields like computer vision, natural language processing, and drug discovery, where generating novel content or simulating data is crucial.