Deep learning

Step by step with explanation please

I don't want the code only, I need the full answer for it. use the values in the questions and upload screenshots of the output screen please.

you can use this code below to answer or any other code that you have

3. For the input space x the following 4x5 weight matrix (w) is given. Calculate the Softmax output. Bias b = [0,1; -0,2; 0,3; 0). x should be defined by you from numbers bw 20-50, where some can be negative values i.e. (26, -30, 48, -10, 35) (Do not use this examplary x values!!!) -0,05 0,01 0,03 0,1 0,2 0,22 0,03 0,2 0,02 -0,3 -0,26 0,02 0,01 0.02 0,16 0,48 0,8 -0,35 0,18 0,01 Turkcell LTE 20:12 %100 softmax can be easily found by below code, def softmax(x): return np.exp(x) / np.sum(np.exp(x), axis=0) x = np.random.rand(4,5) print (softmax(x)) here the sum of all exponents are divided from each X