End-to-End Learning

A training approach where a model learns to map raw inputs directly to final outputs, without manually engineered intermediate steps. Rather than decomposing a task into hand-crafted stages, the model figures out the full pipeline from data alone. End-to-end learning became the dominant paradigm in deep learning, replacing earlier pipelines that depended heavily on domain-specific feature engineering.
See also: deep learning, feature engineering, supervised learning.