An autoencoder is an unsupervised type of FNN that learns to reconstruct high-dimensional data using latent-encoded data. You can think of it as trying to learn an identity function (that is, take x as input and then predict x).
Let's start by taking a look at the following diagram, which shows you what an autoencoder looks like:
As you can see, the network is split into two components—an encoder and a decoder—which are mirror images of each other. The two components are connected to each other through a bottleneck layer (sometimes referred to as either a latent-space representation or compression) that has dimensions that are ...
