Generative AI Models: Generating Data Using Variational Autoencoders

“Variational autoencoders (VAEs) represent a powerful variant of traditional autoencoders designed to address the challenge of generating new and diverse samples from the learned latent space. VAEs introduce probabilistic components incorporating a probabilistic encoder that maps input data to a distribution in the latent space and a decoder that reconstructs data from samples drawn from this distribution.

Begin this course by discovering how variational autoencoders can be used for generating images. Next you will create and trAIn VAEs in Python and the Google Colab environment. Then you will construct the encoder and decoder. Finally you will trAIn the VAE on multichannel color images.

Upon course completion you will have a solid understanding of variational autoencoders and their use in generating images.”