diff --git a/costFunction.m b/costFunction.m
index 94336bb..4234ffb 100644
--- a/costFunction.m
+++ b/costFunction.m
@@ -1,25 +1,15 @@
 function [J, grad] = costFunction(theta, X, y)
-%COSTFUNCTION Compute cost and gradient for logistic regression
+%COSTFUNCTION Computing cost and gradient for logistic regression
 %   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
 %   parameter for logistic regression and the gradient of the cost
 %   w.r.t. to the parameters.
 
-% Initialize some useful values
+% Initializing some useful values
 m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
+ 
 J = 0;
 grad = zeros(size(theta));
 
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta.
-%               You should set J to the cost.
-%               Compute the partial derivatives and set grad to the partial
-%               derivatives of the cost w.r.t. each parameter in theta
-%
-% Note: grad should have the same dimensions as theta
-%
-
 h = sigmoid(X*theta);
 y1 = -y.*log(h);
 y0 = -(1-y).*log(1-h);
@@ -28,7 +18,4 @@
 
 grad = (X'*(h-y))/m;
 
-
-% =============================================================
-
 end