Deep learning Methods that are used to train models with several levels of abstraction from the raw input to the output. For example, in visual recognition, the lowest level is an image composed of pixels. In layers as we go up, a deep learner combines them to form strokes and edges of different orientations, which can then be combined to detect longer lines, arcs, corners, and junctions, which in turn can be combined to form rectangles, circles, and so on. The units of each layer may be thought of as a set of primitives at a different level of abstraction.
