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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...   #variationalinference #latentvariable #variationalautoencoder