 For supervised learning tasks, deep learning methods eliminate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures that remove redundancy in representation.Deep learning algorithms can be applied to unsupervised learning tasks.This is an important benefit because unlabeled data are more abundant than the labeled data.