Answers.Body: <p>Yes it applies, and no it shouldn't matter that much between the two activation functions.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-25T09:46:01.510Z
Answers.Text: So the difference between the two is basically whether the range of data is between <0; 1> or <-1; 1>?
Answers.UserDisplayName: gnivirht
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-25T03:19:50.767Z
Answers.LastActivityDate: 2018-01-25T03:19:50.767Z
Answers.UserDisplayName: k.c. sayz 'k.c sayz'
Body: <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>
  
Tags: <neural-networks><datasets>
Title: Data-set values feature scaling: sigmoid vs tanh
UserDisplayName: gnivirht
ThreadText: # 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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