Nobody labelled the edges

The labels say only "cat" or "dog." They do not provide a list of intermediate checks.

Training changes the hidden layers until the signals they produce make the final distinction easier.

In one common picture of an image network, early layers respond to simple patterns such as edges. Later layers combine them into corners, curves, and parts of objects.

The new version of the input that a layer passes onward is called a .

The edge-to-shape ladder is an example, not a rule every network follows. The patterns depend on the network structure, the examples, and the task.

People name the final task. Training shapes the steps in between.

Nobody labelled the edges

The labels say only "cat" or "dog." They do not provide a list of intermediate checks.

Training changes the hidden layers until the signals they produce make the final distinction easier.

In one common picture of an image network, early layers respond to simple patterns such as edges. Later layers combine them into corners, curves, and parts of objects.

The new version of the input that a layer passes onward is called a .

The edge-to-shape ladder is an example, not a rule every network follows. The patterns depend on the network structure, the examples, and the task.

People name the final task. Training shapes the steps in between.