Variational Autoencoders are a type of generative model used in unsupervised learning. VAEs learn a low-dimensional representation of input data and can generate new data samples similar to the training data. They have applications in tasks such as image generation, anomaly detection, and data compression.
Data Preprocessing involves preparing and cleaning raw data before analysis. By removing noise, selecting relevant features, and addressing missing values, businesses can ensure data…