Slight doc rewrite

doc/tutorial/loading_and_saving.txt

@@ -117,20 +117,25 @@ For instance, you can define functions along the lines of:
 Robust Serialization
 ====================
 
-This type of serialization internally uses pickling but extracts values of all
-arrays (`CudaNdarray`, `ndarray`) and saves it as numpy arrays. So it may be
-very hard to deserialize objects on a different version of Theano.
-But tt is useful when you would like to resume an experiment on a different
-kind of hardware.
+This type of serialization uses some helper functions particular to Theano. It
+serializes the object using Python's pickling protocol, but any ``ndarray`` or
+``CudaNdarray`` objects contained within the object are saved separately as NPY
+files. These NPY files and the Pickled file are all saved together in single
+ZIP-file.
 
-Another use case of this type of serialization is to use parameters for another
-model after pretraining, fixing bugs or minor changes in it. You can still
-load the parameters manually with `numpy`:
+The main advantage of this approach is that you don't even need Theano installed
+in order to look at the values of shared variables that you pickled. You can
+just load the parameters manually with `numpy`.
 
 .. code-block:: python
 
     numpy.load('model.zip')
 
+This approach could be beneficial if you are sharing your model with people who
+might not have Theano installed, who are using a different Python version, or if
+you are planning to save your model for a long time (in which case version
+mismatches might make it difficult to unpickle objects).
+
 .. autofunction:: theano.misc.pkl_utils.dump
 
 .. autofunction:: theano.misc.pkl_utils.load