Fix markdown formatting.

Change: 139075656

2 files changed, 30 insertions, 18 deletions

diff --git a/tensorflow/g3doc/resources/uses.md b/tensorflow/g3doc/resources/uses.md
index 3cc7578206..1d2f3bb811 100644
--- a/tensorflow/g3doc/resources/uses.md
+++ b/tensorflow/g3doc/resources/uses.md
@@ -11,28 +11,38 @@ This page describes some of the current uses of the TensorFlow system.
 Listed below are some of the many uses of TensorFlow.
 
 * **RankBrain**
-  * **Organization**: Google
-  * **Domain**: Information Retrieval
-  * **Description**:  A large-scale deployment of deep neural nets for search ranking on www.google.com.
-  * **More info**: ["Google Turning Over Its Lucrative Search to AI Machines"](http://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines)
+<ul>
+   <li>**Organization**: Google</li>
+   <li> **Domain**: Information Retrieval</li>
+   <li> **Description**:  A large-scale deployment of deep neural nets for search ranking on www.google.com.</li>
+   <li> **More info**: ["Google Turning Over Its Lucrative Search to AI Machines"](http://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines)</li>
+</ul>
 
 * **Inception Image Classification Model**
-  * **Organization**: Google
-  * **Description**: Baseline model and follow on research into highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge
-  * **More Info**: Baseline model described in [Arxiv paper](http://arxiv.org/abs/1409.4842)
+<ul>
+   <li> **Organization**: Google</li>
+   <li> **Description**: Baseline model and follow on research into highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge</li>
+   <li> **More Info**: Baseline model described in [Arxiv paper](http://arxiv.org/abs/1409.4842)</li>
+</ul>
 
 * **SmartReply**
-  * **Organization**: Google
-  * **Description**: Deep LSTM model to automatically generate email responses
-  * **More Info**: [Google research blog post](http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html)
+<ul>
+  <li> **Organization**: Google</li>
+  <li> **Description**: Deep LSTM model to automatically generate email responses</li>
+  <li> **More Info**: [Google research blog post](http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html)</li>
+</ul>
 
 * **Massively Multitask Networks for Drug Discovery**
-  * **Organization**: Google and Stanford University
-  * **Domain**: Drug discovery
-  * **Description**:  A deep neural network model for identifying promising drug candidates.
-  * **More info**: [Arxiv paper](http://arxiv.org/abs/1502.02072)
+<ul>
+  <li> **Organization**: Google and Stanford University</li>
+  <li> **Domain**: Drug discovery</li>
+  <li> **Description**:  A deep neural network model for identifying promising drug candidates.</li>
+  <li> **More info**: [Arxiv paper](http://arxiv.org/abs/1502.02072)</li>
+</ul>
 
 * **On-Device Computer Vision for OCR**
-  * **Organization**: Google
-  * **Description**: On-device computer vision model to do optical character recognition to enable real-time translation.
-  * **More info**: [Google Research blog post](http://googleresearch.blogspot.com/2015/07/how-google-translate-squeezes-deep.html)
+<ul>
+  <li> **Organization**: Google</li>
+  <li> **Description**: On-device computer vision model to do optical character recognition to enable real-time translation.</li>
+  <li> **More info**: [Google Research blog post](http://googleresearch.blogspot.com/2015/07/how-google-translate-squeezes-deep.html)</li>
+</ul>
diff --git a/tensorflow/g3doc/tutorials/deep_cnn/index.md b/tensorflow/g3doc/tutorials/deep_cnn/index.md
index ed431eaa37..9f44295c28 100644
--- a/tensorflow/g3doc/tutorials/deep_cnn/index.md
+++ b/tensorflow/g3doc/tutorials/deep_cnn/index.md
@@ -7,7 +7,9 @@ and assumes expertise and experience in machine learning.
 
 CIFAR-10 classification is a common benchmark problem in machine learning.  The
 problem is to classify RGB 32x32 pixel images across 10 categories:
-```airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.```
+```
+airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
+```
 
 For more details refer to the [CIFAR-10 page](http://www.cs.toronto.edu/~kriz/cifar.html)
 and a [Tech Report](http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)