title: Normalizing to [0,1] vs [-1,1]
body: <p>I've been going through a few tutorials on using neural networks for key points detection. I've noticed that for the inputs (images) it's very common to divide by 255 (normalizing to [0,1] since values fall between 0 and 255). But for the targets (X/Y) coordinates I've noticed it's more common to normalize to [-1,1]. Any reason for this disparity.</p>
  
  <p>Example: <a href="http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/" rel="noreferrer">http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/</a></p>
  
  <pre><code>X = np.vstack(df['Image'].values) / 255.  # scale pixel values to [0, 1]
  y = (y - 48) / 48  # scale target coordinates to [-1, 1]
  </code></pre>