{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Variational Autoencoders\n", "\n", "## Introduction\n", "\n", "The variational autoencoder (VAE) is arguably the simplest setup that realizes deep probabilistic modeling. Note that we're being careful in our choice of language here. The VAE isn't a model as such—rather the VAE is a particular setup for doing variational inference for a certain class of models. The class of models is quite broad: basically\n", "any (unsupervised) density estimator with latent random variables. The basic structure of such a model is simple, almost deceptively so (see Fig. 1)." ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/html" }, "source": [ "
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