Variational Autoencoder - VISUALLY EXPLAINED!

This tutorial provides an in-depth explanation of 3 big but related ideas in machine learning - Latent Variable Model, Amortized Inference and Variational Autoencoder.

Most of the time VAE is explained as an Autoencoder where the latent vector has a distribution however that explanation misses the main goals behind its motivation.

First, we see what it means to have an amortized inference and how a certain category of models called Latent Variable Models requires it in order to be efficient when we deal with large datasets.

Then we construct a neural network that addresses the challenges with Latent Variable Models leading to the creation of VAE.

Recommended videos to watch before this one

KL Divergence
   • KL Divergence - CLEARLY EXPLAINED!  

Evidence Lower Bound
   • Evidence Lower Bound (ELBO) - CLEARLY...  

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