by Amit Shekhar · 2 June 2026
Variational Autoencoders
Variational Autoencoders. We will understand what they are, why we need them, how they work step by step, and how they are able to generate brand new data like images that never existed before.
Read on Outcome School ↗then come back to lock it in
Before you read, guessWhat model learns a smooth latent space to generate new data from random points?
Ten seconds, a guess, then read — a wrong guess still makes the answer stick.
What this article covers
- What is an Autoencoder?
- The problem with a normal Autoencoder
- What is a Variational Autoencoder?
- The encoder, the latent space, and the decoder
- The reparameterization trick
- The loss function of a Variational Autoencoder
- A simple example walk-through
- A simple code example
- Advantages of Variational Autoencoders
- Where Variational Autoencoders are used
The article lives on outcomeschool.com. Read it there, then come back: the tutor in the margin has read it and will answer questions, and the questions below check what stayed.