doc fix following review.

doc/library/sandbox/cuda/op.txt

@@ -6,7 +6,7 @@
 
 .. moduleauthor:: LISA
 
-Normally you should not call directly those Ops! Theano should automatically transform cpu ops to there gpu equivalent. So this list is just useful to let people know what is implemented on the gpu.
+Normally you should not call directly those Ops! Theano should automatically transform cpu ops to their gpu equivalent. So this list is just useful to let people know what is implemented on the gpu.
 
 Basic Op
 ========

doc/tutorial/gradients.txt

@@ -94,12 +94,12 @@ of symbolic differentiation).
 Computing the Jacobian
 ======================
 
-Theano implement :func:`theano.gradient.jacobian` macro that does all
-what is needed to compute the jacobian. The following text explain how
+Theano implements :func:`theano.gradient.jacobian` macro that does all
+what is needed to compute the Jacobian. The following text explains how
 to do it manually.
 
 In order to manually compute the Jacobian of some function ``y`` with
-respect to some parameter ``x`` we need to use the ``scan``. What we
+respect to some parameter ``x`` we need to use ``scan``. What we
 do is to loop over the entries in ``y`` and compute the gradient of
 ``y[i]`` with respect to ``x``.
 
@@ -138,13 +138,13 @@ matrix, which corresponds to the Jacobian.
 Computing the Hessian
 =====================
 
-Theano implement :func:`theano.gradient.hessian` macro that does all
-what is needed to compute the hessian. The following text explain how
+Theano implements :func:`theano.gradient.hessian` macro that does all
+that is needed to compute the Hessian. The following text explains how
 to do it manually.
 
-You can compute the hessian manually as the jacobian. The only
+You can compute the Hessian manually as the Jacobian. The only
 difference is that now, instead of computing the Jacobian of some expression
-``y``, we compute the jacobian of ``T.grad(cost,x)``, where ``cost`` is some
+``y``, we compute the Jacobian of ``T.grad(cost,x)``, where ``cost`` is some
 scalar.