Deep learning excels at value-centric abstraction and is fundamentally a geometrical method performing smooth geometric morphing between input and target vector spaces via gradient descent, learning a distance function that defines a manifold; it is therefore well-suited to perception problems but cannot solve discrete problems requiring program-centric abstraction.