<p>Yes it applies, and no it shouldn't matter that much between the two activation functions.</p>

CC BY-SA 3.0
2018-01-25T09:46:01.510Z
So the difference between the two is basically whether the range of data is between <0; 1> or <-1; 1>?
gnivirht
CC BY-SA 3.0
2018-01-25T03:19:50.767Z
2018-01-25T03:19:50.767Z
k.c. sayz 'k.c sayz'
<p>As many papers point out, for better learning curve of a NN, it is better for a data-set to be normalized in a way such that values match a Gaussian curve.</p>

<p>Does this process of feature normalization apply only if we use sigmoid function as squashing function? If not what deviation is best for the tanh squashing function?</p>

<neural-networks><datasets>
Data-set values feature scaling: sigmoid vs tanh
gnivirht
# Data-set values feature scaling: sigmoid vs tanh

(Asked by: username_0 on 2018-01-24)

As many papers point out, for better learning curve of a NN, it is better for a data-set to be normalized in a way such that values match a Gaussian curve.

Does this process of feature normalization apply only if we use sigmoid function as squashing function? If not what deviation is best for the tanh squashing function?

**Tags:** neural-networks, datasets


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## 1 answers

### Answer by username_1 on 2018-01-25. Score: 0

Yes it applies, and no it shouldn't matter that much between the two activation functions.

> **Comments:**
> **username_0**: So the difference between the two is basically whether the range of data is between <0; 1> or <-1; 1>?  

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