Autoencoder Decoder Notes Cheatsheet from Udacity Deep Learning Nanodegree

• Auto encoder decoder are two neural networks that are trained to encode, and then decode the input based on training data and examples. The goal is to compress with little loss and decode to get as close to the original as possible while getting performance gain and efficiency. 
• Encode is to obtain a compressed representation of the input
• Decode is takes the representation from encoders and try to reconstruct the original input
• A compressed representation can save space, time, and improve performance of  storage, serving and other computer, network tasks 
• The depth dimensions should change as follows: 784 inputs > encoding_dim > 784 outputs.