diff --git a/getting_started/04_numpy.ipynb b/getting_started/04_numpy.ipynb
index e877863b0d6105f54087da9a1759aab2a4cc8b31..8c7f1d7499452a82050d9a10fa9fc72cdb8bff32 100644
--- a/getting_started/04_numpy.ipynb
+++ b/getting_started/04_numpy.ipynb
@@ -775,8 +775,14 @@
    "source": [
     "> Here we used a handy feature of the `reshape` method - if you pass `-1` for\n",
     "> the size of one dimension, it will automatically determine the size to use\n",
-    "> for that dimension. Take a look at [the\n",
-    "> appendix](#appendix-vectors-in-numpy) for a discussion on vectors in Numpy.\n",
+    "> for that dimension.\n",
+    "\n",
+    "\n",
+    "Note that Numpy treats row and column vectors differently than in Matlab,\n",
+    "which is a potential source of confusion for those of you who are used to\n",
+    "Matlab's linear algebra-based take on things. You might wish to take a break\n",
+    "now to read [the appendix](#appendix-vectors-in-numpy) for a discussion on\n",
+    "vectors in Numpy.\n",
     "\n",
     "\n",
     "Here is a more useful example, where we use broadcasting to de-mean the rows\n",
diff --git a/getting_started/04_numpy.md b/getting_started/04_numpy.md
index dce3532bb6e37ba55f749cd3f7d25cf97878c7da..a97a6945763622dfcbe1e0834e7bcf175b83c6d9 100644
--- a/getting_started/04_numpy.md
+++ b/getting_started/04_numpy.md
@@ -576,8 +576,14 @@ print(a * b.reshape(-1, 1))
 
 > Here we used a handy feature of the `reshape` method - if you pass `-1` for
 > the size of one dimension, it will automatically determine the size to use
-> for that dimension. Take a look at [the
-> appendix](#appendix-vectors-in-numpy) for a discussion on vectors in Numpy.
+> for that dimension.
+
+
+Note that Numpy treats row and column vectors differently than in Matlab,
+which is a potential source of confusion for those of you who are used to
+Matlab's linear algebra-based take on things. You might wish to take a break
+now to read [the appendix](#appendix-vectors-in-numpy) for a discussion on
+vectors in Numpy.
 
 
 Here is a more useful example, where we use broadcasting to de-mean the rows