Data pre-processing is a necessary step before any neural network can successfully ingest and analyze that data. The methods used to do this pre-processing are critical to the network’s performance.
Traditional data pre-processing methods include mean subtraction, normalization, and whitening, which have been around a long time—well before batch normalization came into the picture, which is what we’ll focus on in this post. To start, let’s define these pre-processing methods.