diff --git a/ex1/computeCostMulti.m b/ex1/computeCostMulti.m
index d9a3d68..5f59913 100644
--- a/ex1/computeCostMulti.m
+++ b/ex1/computeCostMulti.m
@@ -6,7 +6,7 @@ function J = computeCostMulti(X, y, theta)
 % Initialize some useful values
 m = length(y); % number of training examples
 
-% You need to return the following variables correctly 
+% You need to return the following variables correctly
 J = 0;
 
 % ====================== YOUR CODE HERE ======================
@@ -15,8 +15,6 @@ J = 0;
 
 
 
-
-
 % =========================================================================
 
 end
diff --git a/ex1/gradientDescentMulti.m b/ex1/gradientDescentMulti.m
index e5e1b7e..c98315b 100644
--- a/ex1/gradientDescentMulti.m
+++ b/ex1/gradientDescentMulti.m
@@ -11,7 +11,7 @@ for iter = 1:num_iters
 
     % ====================== YOUR CODE HERE ======================
     % Instructions: Perform a single gradient step on the parameter vector
-    %               theta. 
+    %               theta.
     %
     % Hint: While debugging, it can be useful to print out the values
     %       of the cost function (computeCostMulti) and gradient here.
@@ -19,17 +19,9 @@ for iter = 1:num_iters
 
 
 
-
-
-
-
-
-
-
-
     % ============================================================
 
-    % Save the cost J in every iteration    
+    % Save the cost J in every iteration
     J_history(iter) = computeCostMulti(X, y, theta);
 
 end