Variational Autoencoders
Save
May 1, 2024
3 minute read
Variational Autoencoders (VAEs) are a type of generative model that can be used to learn the distribution of a dataset and generate new data points from that distribution. VAEs are based on the idea of variational inference, which is a technique for approximating intractable integrals. In the case of VAEs, the intractable integral is the posterior distribution of the latent variables given the observed data. VAEs are often used for tasks such as image generation, text generation, and music generation.
Why Learn Variational Autoencoders
Read More
Find a path to becoming a Variational Autoencoders. Learn more at:
OpenCourser.com/topic/448f3c/variational
