What are generative models in ML?

Generative models in machine learning refer to a class of models that aim to capture the underlying probability distribution of the data. By learning the data distribution, generative models can then generate new samples that are similar to the original data.

How do generative models work?

Generative models can be implemented using various techniques such as deep learning, neural networks, and Bayesian methods. These models learn the patterns and features present in the training data to generate new samples that exhibit similar characteristics.

Applications of generative models

Generative models have numerous applications in machine learning, such as synthetic data generation, data augmentation, computer vision, natural language processing, and more. These models can be used to create new data instances for training models, enhance the diversity of datasets, or even generate images or text.

Overall, generative models play a crucial role in advancing the field of artificial intelligence by enabling the synthesis of data and the generation of new samples based on learned probability distributions.