input space has the exact same f-fine mapping okay and this is what happens when you learn just to see when you if you initialize with random random weights zero biases you just get a set of cones and as we go through learning epochs you see that we end up with these cones pulling away from the origin and then cones being cut up by other cones and we result again and for at least layers wanted to this particular mapping it at convergence okay and I'm gonna III think that it's really thinking of this geometrical