Merge pull request Esri#513 from Yongyao/yong9856-2516

Updated Unet sample final result

Author: Atma Mani

guide/14-deep-learning/how-unet-works.ipynb

@@ -66,7 +66,7 @@
     "- The decoder is the second half of the architecture. The goal is to semantically project the discriminative features (lower resolution) learnt by the encoder onto the pixel space (higher resolution) to get a dense classification. The decoder consists of **upsampling** and **concatenation** followed by regular convolution operations. \n",
     "\n",
     "<center><img src=\"../../static/img/unet.png\" height=\"600\" width=\"600\"></center>\n",
-    "<center>Figure 2. U-net architecture. Blue boxes represent multi-channel feature maps, while while boxes represent copied feature maps. The arrows of different colors represent different operations</center>"
+    "<center>Figure 2. U-net architecture. Blue boxes represent multi-channel feature maps, while while boxes represent copied feature maps. The arrows of different colors represent different operations [1]</center>"
    ]
   },
   {