Fixed a few typos / grammar errors in doc

doc/library/compile/function.txt

@@ -149,7 +149,7 @@ Reference
     will be used as the update expression for this variable.  Passing
     ``no_default_updates=True`` to ``function`` disables this behavior
     entirely, passing ``no_default_updates=[sharedvar1, sharedvar2]``
-    disables it for the mentionned variables.
+    disables it for the mentioned variables.
 
     Regarding givens: Be careful to make sure that these substitutions are
     independent--behaviour when Var1 of one pair appears in the graph leading

doc/library/tensor/shared_randomstreams.txt

@@ -16,11 +16,11 @@ Since Theano uses a functional design, producing pseudo-random numbers in a
 graph is not quite as straightforward as it is in numpy. If you are using Theano's
 shared variables, then a `RandomStreams` object is probably what you want.  (If you are
 using Module then this tutorial will be useful but not exactly what you want.  
-Have a look at the :api:`RandomFunction` Op.)
+Have a look at the :class:`RandomFunction` Op.)
 
-The way to think about putting randomness into theano's computations is to
+The way to think about putting randomness into Theano's computations is to
 put random variables in your graph.  Theano will allocate a numpy RandomState
-object for each such variable, and draw from it as necessary.  I'll call this sort of sequence of
+object for each such variable, and draw from it as necessary.  We will call this sort of sequence of
 random numbers a *random stream*.
 
 For an example of how to use random numbers, see
@@ -38,8 +38,8 @@ Reference
 
     .. method:: updates()
 
-        :returns: a list of all the (state, new_state) update pairs from the
-          random variables it has returned.  
+        :returns: a list of all the (state, new_state) update pairs for the
+          random variables created by this object
           
         This can be a convenient shortcut to enumerating all the random
         variables in a large graph in the ``update`` parameter of function.
@@ -47,8 +47,8 @@ Reference
     .. method:: seed(meta_seed)
 
         `meta_seed` will be used to seed a temporary random number generator,
-        that will in turn generate seeds for each of the random variables that
-        has been created by this object (via `gen`).
+        that will in turn generate seeds for all random variables
+        created by this object (via `gen`).
 
         :returns: None
 

doc/tutorial/examples.txt

@@ -377,11 +377,11 @@ Here's a brief example.  The setup code is:
     nearly_zeros = function([], rv_u + rv_u - 2 * rv_u)
 
 Here, 'rv_u' represents a random stream of 2x2 matrices of draws from a uniform
-distribution.  Likewise,  'rv_n' represenents a random stream of 2x2 matrices of
+distribution.  Likewise,  'rv_n' represents a random stream of 2x2 matrices of
 draws from a normal distribution.  The distributions that are implemented are
 defined in :class:`RandomStreams`.
 
-Now let's use these things.  If we call f(), we get random uniform numbers.
+Now let's use these objects.  If we call f(), we get random uniform numbers.
 The internal state of the random number generator is automatically updated,
 so we get different random numbers every time.
 
@@ -394,7 +394,7 @@ not affected by calling the returned function.  So for example, calling
 ``g`` multiple times will return the same numbers.
 
 >>> g_val0 = g()  # different numbers from f_val0 and f_val1
->>> g_val1 = g()  # same numbers as g_val0 !!!
+>>> g_val1 = g()  # same numbers as g_val0!
 
 An important remark is that a random variable is drawn at most once during any
 single function execution.  So the ``nearly_zeros`` function is guaranteed to