minor typos

doc/pub/week39/html/week39-reveal.html

@@ -1774,7 +1774,7 @@ $$
 
 <p>This implies that the Hessian matrix  is positive definite, hence the stationary point is a
 minimum.
-Note that the Ridge loss function is convex, as a sum of two convex
+Note that the Ridge cost function is convex being  a sum of two convex
 functions. Therefore, the stationary point is a global
 minimum of this function.
 </p>

doc/pub/week39/html/week39-solarized.html

@@ -1681,7 +1681,7 @@ $$
 
 <p>This implies that the Hessian matrix  is positive definite, hence the stationary point is a
 minimum.
-Note that the Ridge loss function is convex, as a sum of two convex
+Note that the Ridge cost function is convex being  a sum of two convex
 functions. Therefore, the stationary point is a global
 minimum of this function.
 </p>

doc/pub/week39/html/week39.html

@@ -1758,7 +1758,7 @@ $$
 
 <p>This implies that the Hessian matrix  is positive definite, hence the stationary point is a
 minimum.
-Note that the Ridge loss function is convex, as a sum of two convex
+Note that the Ridge cost function is convex being  a sum of two convex
 functions. Therefore, the stationary point is a global
 minimum of this function.
 </p>

doc/src/week39/week39.do.txt

@@ -1177,7 +1177,7 @@ The Hessian matrix of Ridge Regression for our simple example  is given by
 !et
 This implies that the Hessian matrix  is positive definite, hence the stationary point is a
 minimum.
-Note that the Ridge loss function is convex, as a sum of two convex
+Note that the Ridge cost function is convex being  a sum of two convex
 functions. Therefore, the stationary point is a global
 minimum of this function.