In this paper I will show how neural nets can be used to detect redundant information in input data. Once redundant information is detected, the data can be compressed. This can sometimes greatly simplify and speed up goal directed learning. Three methods for redundancy reduction will be presented. All of them are based on unsupervised networks that learn to ``predict away'' redundant information.