title: Why use tanh for activation function of MLP?
body: <p>Im personally studying theories of neural network and got some questions. </p>
  
  <p>In many books and references, for activation function of hidden layer, hyper-tangent functions were used. </p>
  
  <p>Books came up with really simple reason that linear combinations of tanh functions can describe nearly all shape of functions with given error. </p>
  
  <p>But, there came a question. </p>
  
  <ol>
  <li>Is this a real reason why tanh function is used?</li>
  <li>If then, is it the only reason why tanh function is used?</li>
  <li>if then, is tanh function the only function that can do that?</li>
  <li>if not, what is the real reason?..</li>
  </ol>
  
  <p>I stock here keep thinking... please help me out of this mental(?...) trap!</p>