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