2) Given w and input pattern x1 = (1,0,1,0,0), x2 = (0,0,0,1,1) and x3 = (0,1,1,0,0) output and perform new weight calculations according to Hebb learning rule. (bias / noise zero and learning coefficient 1 can be assumed).

3) 4x5 dimension weight vector (w) is given for x input space. Calculate the softmax output. Bias b = [0.1; -0.2; 0.3; 0]. x is one of the numbers between 20 and 50 by you, as in the 1x5 size example will be created, eg: (26, -30, 48, -10, 35) (Do not use this example x input !!!).

1 1 0 W=120 02 LO 1 -0.05 02 0.03 -0.3 0.02 0.16 0.48 0.26 0.01 008 002 0.02 0.03 0.22 0.18 0.01 0.1 -0.35 002 001