Body: <p>Why is it that the skewed contour (unscaled features) will result in slow performance of gradient descent? In other words, how (or why) will the gradients end up taking a long time before finding the global minimum in such cases? This might be an obvious question but I'm finding a hard time Visualizing the 3D shapes of the respective contours and relating it to the convergence.
  <a href="https://i.sstatic.net/6VjAu.gif" rel="nofollow noreferrer"><img src="https://i.sstatic.net/6VjAu.gif" alt=" " /></a></p>
  <p><code>Left one is the contour for the unscaled feature and the right one is scaled (and will apparently converge quickly).</code></p>
  
Tags: <neural-networks><machine-learning><gradient-descent>
Title: Why Feature Scaling for skewed contour?
UserDisplayName: Akash Adhikari
ThreadText: # Why Feature Scaling for skewed contour?
  
  (Asked by: username_0 on 2018-06-23)
  
  Why is it that the skewed contour (unscaled features) will result in slow performance of gradient descent? In other words, how (or why) will the gradients end up taking a long time before finding the global minimum in such cases? This might be an obvious question but I'm finding a hard time Visualizing the 3D shapes of the respective contours and relating it to the convergence.
  [![ ](https://i.sstatic.net/6VjAu.gif)](https://i.sstatic.net/6VjAu.gif)
  
  `Left one is the contour for the unscaled feature and the right one is scaled (and will apparently converge quickly).`
  
  **Tags:** neural-networks, machine-learning, gradient-descent
  
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  ## 0 answers
  
  