Moved broadcasting docs from beginning of elemwise section to end.

doc/library/tensor/basic.txt

@@ -656,47 +656,6 @@ computations.  If you would like to update the value of a
 Elementwise
 ===========
 
-.. _libdoc_tensor_broadcastable:
-
-Broadcasting in Theano vs. Numpy
---------------------------------
-
-Broadcasting is a mechanism which allows tensors with
-different numbers of dimensions to be added or multiplied
-together by (virtually) replicating the smaller tensor along
-the dimensions that it is lacking.
-
-Broadcasting is the mechanism by which a scalar
-may be added to a matrix, a vector to a matrix or a scalar to
-a vector.
-
-.. figure:: bcast.png
-
-Broadcasting a row matrix. T and F respectively stand for
-True and False and indicate along which dimensions we allow
-broadcasting.
-
-If the second argument were a vector, its shape would be
-``(2,)`` and its broadcastable pattern ``(F,)``. They would
-be automatically expanded to the **left** to match the
-dimensions of the matrix (adding ``1`` to the shape and ``T``
-to the pattern), resulting in ``(1, 2)`` and ``(T, F)``.
-It would then behave just like the example above.
-
-
-Unlike numpy which does broadcasting dynamically, Theano needs
-to know, for any operation which supports broadcasting, which
-dimensions will need to be broadcasted. When applicable, this
-information is given in the :ref:`type` of a *Variable*.
-
-See also:
-
-* `SciPy documentation about numpy's broadcasting <http://www.scipy.org/EricsBroadcastingDoc>`_
-
-* `OnLamp article about numpy's broadcasting <http://www.onlamp.com/pub/a/python/2000/09/27/numerically.html>`_
-
-
-
 Casting
 -------
 
@@ -873,6 +832,48 @@ Mathematical
     Returns a variable representing the hyperbolic trigonometric functions of a (hyperbolic cosine, sine and tangent).
 
 
+.. _libdoc_tensor_broadcastable:
+
+Broadcasting in Theano vs. Numpy
+--------------------------------
+
+Broadcasting is a mechanism which allows tensors with
+different numbers of dimensions to be added or multiplied
+together by (virtually) replicating the smaller tensor along
+the dimensions that it is lacking.
+
+Broadcasting is the mechanism by which a scalar
+may be added to a matrix, a vector to a matrix or a scalar to
+a vector.
+
+.. figure:: bcast.png
+
+Broadcasting a row matrix. T and F respectively stand for
+True and False and indicate along which dimensions we allow
+broadcasting.
+
+If the second argument were a vector, its shape would be
+``(2,)`` and its broadcastable pattern ``(F,)``. They would
+be automatically expanded to the **left** to match the
+dimensions of the matrix (adding ``1`` to the shape and ``T``
+to the pattern), resulting in ``(1, 2)`` and ``(T, F)``.
+It would then behave just like the example above.
+
+
+Unlike numpy which does broadcasting dynamically, Theano needs
+to know, for any operation which supports broadcasting, which
+dimensions will need to be broadcasted. When applicable, this
+information is given in the :ref:`type` of a *Variable*.
+
+See also:
+
+* `SciPy documentation about numpy's broadcasting <http://www.scipy.org/EricsBroadcastingDoc>`_
+
+* `OnLamp article about numpy's broadcasting <http://www.onlamp.com/pub/a/python/2000/09/27/numerically.html>`_
+
+
+
+
 
 Linear Algebra
 ==============