<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>

<neural-networks><machine-learning><gradient-descent>
Why Feature Scaling for skewed contour?
Akash Adhikari
# 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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