diff --git a/examples/dnn_introduction2_ex.cpp b/examples/dnn_introduction2_ex.cpp
index 579b8c678..f79463089 100644
--- a/examples/dnn_introduction2_ex.cpp
+++ b/examples/dnn_introduction2_ex.cpp
@@ -105,7 +105,7 @@ template <typename SUBNET> using ares_down = relu<residual_down<block,8,affine,S
 
 // Now that we have these convenient aliases, we can define a residual network
 // without a lot of typing.  Note the use of a repeat layer.  This special layer
-// type allows us to type repeat<9,res<SUBNET>> instead of
+// type allows us to type repeat<9,res,SUBNET> instead of
 // res<res<res<res<res<res<res<res<res<SUBNET>>>>>>>>>.  It will also prevent
 // the compiler from complaining about super deep template nesting when creating
 // large networks.
diff --git a/tools/imglab/README.txt b/tools/imglab/README.txt
index b2ced92ff..3f0ca92a1 100644
--- a/tools/imglab/README.txt
+++ b/tools/imglab/README.txt
@@ -4,7 +4,7 @@ to train an object detector (e.g. a face detector) since it allows you to
 easily create the needed training dataset.   
 
 You can compile imglab with the following commands:
-    cd dclib/tools/imglab
+    cd dlib/tools/imglab
     mkdir build
     cd build
     cmake ..