Author: jinyang
Date: Thu Sep 10 07:59:47 2015
New Revision: 1702184

URL: http://svn.apache.org/r1702184
Log:
fixed the image linkage problem in overview

Modified:
    incubator/singa/site/trunk/content/markdown/docs/overview.md

Modified: incubator/singa/site/trunk/content/markdown/docs/overview.md
URL: 
http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/overview.md?rev=1702184&r1=1702183&r2=1702184&view=diff
==============================================================================
--- incubator/singa/site/trunk/content/markdown/docs/overview.md (original)
+++ incubator/singa/site/trunk/content/markdown/docs/overview.md Thu Sep 10 
07:59:47 2015
@@ -43,18 +43,9 @@ SINGA comes with a programming model des
 is intuitive for deep learning models.  A variety of
 popular deep learning models can be expressed and trained using this 
programming model.
 
-
-{% comment %}
-consists of multiple layers.  Each layer is associated with a feature
-transformation
-function. After going through all layers, the raw input feature (e.g., pixels
-of images) would be converted into a high-level feature that is easier for
-tasks like classification.
-{% endcomment %}
-
 ## System overview
 
-<img src="http://singa.incubator.apache.org/assets/image/sgd.png"; 
align="center" width="400px"/>
+<img src="http://singa.incubator.apache.org/images/sgd.png"; align="center" 
width="400px"/>
 <span><strong>Figure 1 - SGD flow.</strong></span>
 
 Training a deep learning model is to find the optimal parameters involved in
@@ -67,7 +58,7 @@ closed form solution. Typically, people
 initializes the parameters and then iteratively updates them to reduce the loss
 as shown in Figure 1.
 
-<img src="http://singa.incubator.apache.org/assets/image/overview.png"; 
align="center" width="400px"/>
+<img src="http://singa.incubator.apache.org/images/overview.png"; 
align="center" width="400px"/>
 <span><strong>Figure 2 - SINGA overview.</strong></span>
 
 SGD is used in SINGA to train


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