diff --git a/machine-learning-ex8/ex8/cofiCostFunc.m b/machine-learning-ex8/ex8/cofiCostFunc.m
index 70896b5..97e7fde 100644
--- a/machine-learning-ex8/ex8/cofiCostFunc.m
+++ b/machine-learning-ex8/ex8/cofiCostFunc.m
@@ -46,7 +46,7 @@ t3 = t2(R==1); % only when movie has been rated.
 temp = sum(t3(:));
 J = (1/2) * (temp);
 
-% adding regularization
+% adding regularization to cost
 x_temp = X.^2;
 theta_temp = Theta.^2;
 reg_x = (lambda / 2) * sum(x_temp(:));
@@ -58,6 +58,12 @@ temp1 = ((Theta*X')' - Y);
 temp1(find(R==0)) = 0; % only when movie has been rated.
 X_grad =  temp1 * Theta;
 Theta_grad = temp1' * X; 
+
+% adding regularization to gradients
+X_temp2 = X.*lambda;
+Theta_temp2 = Theta.*lambda;
+X_grad = X_grad + X_temp2;
+Theta_grad = Theta_grad + Theta_temp2;
 % =============================================================
 
 grad = [X_grad(:); Theta_grad(:)];
diff --git a/machine-learning-ex8/ex8/ex8_cofi.m b/machine-learning-ex8/ex8/ex8_cofi.m
index 57baa20..d8f224f 100644
--- a/machine-learning-ex8/ex8/ex8_cofi.m
+++ b/machine-learning-ex8/ex8/ex8_cofi.m
@@ -40,7 +40,7 @@ ylabel('Movies');
 xlabel('Users');
 
 fprintf('\nProgram paused. Press enter to continue.\n');
-% pause;
+pause;
 
 %% ============ Part 2: Collaborative Filtering Cost Function ===========
 %  You will now implement the cost function for collaborative filtering.
@@ -66,7 +66,7 @@ fprintf(['Cost at loaded parameters: %f '...
          '\n(this value should be about 22.22)\n'], J);
 
 fprintf('\nProgram paused. Press enter to continue.\n');
-% pause;
+pause;
 
 
 %% ============== Part 3: Collaborative Filtering Gradient ==============
diff --git a/machine-learning-ex8/ex8/token.mat b/machine-learning-ex8/ex8/token.mat
index aa3c5d9..9ba66e8 100644
Binary files a/machine-learning-ex8/ex8/token.mat and b/machine-learning-ex8/ex8/token.mat differ