Foundations of Data Science
Variational autoencoders (VAEs) are a class of generative models that use deep learning techniques to encode input data into a compressed representation while also allowing for the generation of new data points. They bridge the gap between traditional autoencoders and probabilistic graphical models, enabling effective feature extraction and data synthesis through a process called variational inference, where they learn to approximate complex distributions.
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