Successfully extracting salient (relevant) features is key to the performance of any machine learning algorithm, of course, and traditional machine learning models rely on input features that may come from a domain expert, or are based on computational feature extraction techniques. Neural networks are able to automatically learn the features from raw data that are most useful for a particular task. For this reason, it's common to consider a neural network as a feature extraction engine: the early layers (those right after the input layer) extract low-level features.
