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I published part 1 of a tutorial that shows how to transform vanilla autoencoders into variational autoencoders

Autoencoders have a number of limitations for generative tasks.
That’s why they need a power-up to become Variational
Autoencoders. In my new video, I explain the first step to
transform an autoencoder into a VAE. Specifically, I discuss how
VAEs use multivariate normal distributions to encode input data
into a latent space and why this is awesome for generative tasks.
Don’t worry – I also explain what multivariate normal
distributions are!

This video is part of a series called “Generating Sound with
Neural Networks”. In this series, you’ll learn how to generate
sound from audio files and spectrograms 🎧 🎧 using Variational
Autoencoders 🤖 🤖

Here’s the video:


https://www.youtube.com/watch?v=b8AzCgY1gZI&list=PL-wATfeyAMNpEyENTc-tVH5tfLGKtSWPp&index=9

submitted by /u/diabulusInMusica

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