An autoencoder learns to compress data from the input layer into smaller code, and then uncompress that code into something that (hopefully) matches the original data. The basic idea behind a standard autoencoder is to encode information automatically, hence the name. The entire network always resembles an hourglass, in terms of its shape, with fewer hidden layers than input and output layers. Everything up to the middle layer is called the encoding part, everything after the middle layer is called the decoding part, and the middle layer itself is called, as you have probably guessed, the code. You can train autoencoders by feeding input data and setting the error status as the difference between the input and what came out. Autoencoders can be built so that encoding weights are the same as decoding weights.