1. Classification and Neural Networks

2. Consider On input +1 means Has got it 0 means unknown -1 Means hasn’t got it So Wings (+1), Beak(+1),Feathers(+1), Engine(-1),Tail(0) represents Object has Wings, a beak and feathers, no engine and we don’t know whether it has a tail.

3. Tail Wings Beak Feathers Engine Bird Plane Glider -0.8 -0.7 -1.6 -0.2 -0.1 2.2 -1.1 0.0 2.8 -1.6 -2.9 1.9 -1.1 -1.3 +1 0

4. Activation Function F(X) = +1 if X >= 0 F(X) = -1 if X < 0

5. Calculate XBird and F(XBird) XBird =1*(-0.8) +0*(-0.2)+1*2.2 + 1 * 2.8+(-1) * (-1.1) = 5.3 GT 0 Therefore F(XBIRD) = 1 Bird = + 1

6. Tail Wings Beak Feathers Engine XBird= 5.3 F(XBird) = +1 since XBird > 0 Plane Glider -0.8 -0.7 -1.6 -0.2 -0.1 2.2 -1.1 0.0 2.8 -1.6 -2.9 1.9 -1.1 -1.3 +1 0

7. Calculate XPlane and F(XPlane) XPlane =1*(-0.7) +0*(-0.1)+1*0.0 + 1 * (- 1.6)+(-1) * (1.9) = -4.2 LT 0 Therefore F(XPlane) = -1 PLANE = -1

8. Tail Wings Beak Feathers Engine Bird XPlane = -4.2 XPlane < 0 so F(Xplane) = -1 Glider -0.8 -0.7 -1.6 -0.2 -0.1 2.2 -1.1 0.0 2.8 -1.6 -2.9 1.9 -1.1 -1.3 +1 0

9. Calculate XGlider and F(XGlider) XGLIDER =1*(-0.6) +0*(-1.1)+1*-1.0 + 1 * -2.9+(-1) * (-1.3) = -4.2 LT 0 Therefore F(XGLIDER) = -1 GLIDER = -1

10. Tail Wings Beak Feathers Engine Bird Plane XGlider = -4.2 XGlider < 0 so F(XGlider) = -1 -0.8 -0.7 -1.6 -0.2 -0.1 2.2 -1.1 0.0 2.8 -1.6 -2.9 1.9 -1.1 -1.3 +1 0

11. So Bird = + 1 Plane = -1 Glider = -1 It must be a Bird

12. Tail Wings Beak Feathers Engine Bird Plane Glider -0.8 -0.7 -1.6 -0.2 -0.1 2.2 -1.1 0.0 2.8 -1.6 -2.9 1.9 -1.1 -1.3 +1 0

13. This is an example of Forward Propagation