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An accessible, illustrated introduction to a complex topic

In recent years, GANs (generative adversarial networks) have been all the rage in the field of deep-learning generative models, leaving VAEs in relative obscurity. But there’s much to gain from a solid footing in variational autoencoders, which tackle similar challenges but use a different architectural foundation. If you were looking for an engaging, accessible way to learn more about VAEs, Joseph and Baptiste Rocca’s introduction hits the spot. They define terms, walk us through the various elements that make up VAEs and how they relate to each other, and add beautiful illustrations for all the visual learners out there.

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Ben Huberman

Editor in Chief, Towards Data Science. Previously: Editorial lead, Automattic & Senior Editor, Longreads. (he/him)