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Variational Autoencoders (VAEs)
A variational autoencoder (VAE) is a Generative AI algorithm that uses deep learning to generate new content,
detect anomalies, and remove noise. This is another class of generative models. To produce fresh data, VAEs
learn the distribution of the data and then sample from it.
Latent Space
Distribution Sample
Input Encoder Decoder Output
Some of the examples of VAEs are as follows:
• It can generate new images like the given training set. For instance, a VAE trained on images of faces can
generate new, realistic-looking faces.
• It can produce new text that follows the same style and structure as the training data, assisting writers with
drafts and ideas.
• It can be used for composing new music pieces or creating sound effects, music composition, etc.
Recurrent Neural Networks (RNNs)
RNNs are a special class of neural networks that excel at handling sequential data, like music or text. They excel
at tasks where the order of the data points is important, as they can remember previous inputs and use this
information to influence current outputs.
Recurrence
Output Layer
Input Layer
Hidden Layer
Introduction to Generative AI 333

