Feature learning is the automatic discovery of useful representations or features directly from raw data, replacing manual feature engineering with representations learned by a model during training. Non-linear activation functions and layered network architectures such as feed-forward networks are what allow feature learning to build increasingly abstract representations across depth. It is foundational to deep learning’s success across vision, language and speech tasks.