Neural Network Training (Part 3): Gradient Calculation
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calculus great im fucked
101LiquidNitrogen 2 months ago
@Patterion
Hey yeah thanks, i had allready figured it out.
Seems like a lot of neural net- teachers assume u know the derivative of the sigmoidfunction by heart or something :p
thanx !
00YURIN00 2 months ago
@00YURIN00
It actually is not a derivative of constant, but derivative of the transfer (sigmoid) function then evaluated with the 1.13 constant as parameter for x.
That is:
f=1/(1+e^(-x)) =>
f'=(e^x)/[e^(2x)+2e^(x)+1] =>
f'(1.13)=0.1845 and then
d=0.25*0.1845=0.046.
f is sigmoid function and f' is its derivative.
The 1.13 is actually 1.1278 so that's why 0.046 instead of 0.045.
Patterion 2 months ago
doesn't the derivative of a constant equal zero ?
i don't get how u get to the 0.045 ... :s
00YURIN00 3 months ago
Really great tutorials!
jasuncion1 6 months ago