fix typo following code review.

theano/configdefaults.py

@@ -28,8 +28,8 @@ AddConfigVar('cast_policy',
 
 # python 2.* define int / int to return int and int // int to return int.
 # python 3* define int / int to return float and int // int to return int.
-# numpy 1.6.1 do as the python 2.*. I think we should not change it faster
-# then numpy. When we will do the transition, we should create an int_warn
+# numpy 1.6.1 behaves as python 2.*. I think we should not change it faster
+# than numpy. When we will do the transition, we should create an int_warn
 # and floatX_warn option.
 AddConfigVar('int_division',
         "What to do when one computes x / y, where both x and y are of "

theano/tensor/tensor_grad.py

@@ -259,8 +259,8 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
     :return: symbolic expression of gradient of `cost` with respect to `wrt`.
              If an element of `wrt` is not differentiable with respect
              to the output, then a zero variable is returned.
-             It return an object of same type as `wrt`: a list/tuple
-             or TensorVariable in all case.
+             It returns an object of same type as `wrt`: a list/tuple
+             or TensorVariable in all cases.
 
     This function is a wrapper around the more general function
     `theano.gradient.grad_sources_inputs``.