added figures

doc/BookChapters/chapter12.do.txt

@@ -39,7 +39,7 @@ several such neurons, and the parameters would add up quickly! Clearly,
 this full connectivity is wasteful and the huge number of parameters
 would quickly lead to possible overfitting.
 
-#FIGURE: [figslides/nn.jpeg, width=500 frac=0.6]  A regular 3-layer Neural Network.
+FIGURE: [figslides/nn.jpeg, width=500 frac=0.6]  A regular 3-layer Neural Network.
 
 
 
@@ -67,7 +67,7 @@ end of the CNN architecture we will reduce the full image into a
 single vector of class scores, arranged along the depth
 dimension. 
 
-#FIGURE: [figslides/cnn.jpeg, width=500 frac=0.6]  A CNN arranges its neurons in three dimensions (width, heigh#t, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D out#put volume of neuron activations. In this example, the red input layer holds the image, so its width and heigh#t would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).
+FIGURE: [figslides/cnn.jpeg, width=500 frac=0.6]  A CNN arranges its neurons in three dimensions (width, heigh#t, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D out#put volume of neuron activations. In this example, the red input layer holds the image, so its width and heigh#t would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).