Representation learning

Representation learning is a machine learning approach where computers automatically discover the most useful ways to represent data so that it can be effectively used for tasks like classification or prediction. Rather than humans manually engineering features or patterns to feed into a model, representation learning allows algorithms to learn hierarchical levels of abstraction—like how a computer vision system might first learn to recognize edges, then shapes, then objects—directly from raw data. This approach has proven remarkably powerful because it mimics how human brains seem to process information, breaking complex problems into simpler, more meaningful components.