In this video, we are going to talk about Generative Modeling with Variational Autoencoders (VAEs). The explanation is going to be simple to understand without a math (or even much tech) background. However, I also introduce more technical concepts for you nerds out there while comparing VAEs with Generative Adversarial Networks (GANs).
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REFERENCES
[1] Math + Intuition behind VAE: [ Ссылка ]
[2] Detailed math in VAE: [ Ссылка ]
[3] VAE’s simply explained: [ Ссылка ]
[4] Code for VAE python: [ Ссылка ]
[5] Under the hood of VAE: [ Ссылка ]
[6] Teaching VAE to generate MNIST: [ Ссылка ]
[7] Conditinoal VAE: [ Ссылка ]
[8] Estimating User location in social media with stacked denoising AutoEncoders (Liu and Inkpen, 2015): [ Ссылка ]
Background vector for thumbnail created by vilmosvarga: [ Ссылка ]
