Small update to the doc.

doc/introduction.txt

@@ -195,11 +195,13 @@ Here is the state of that vision as of December 3th, 2013 (after Theano release
   * How to have `DebugMode` check it? Right now, DebugMode checks the computation non-lazily.
 
 * SIMD parallelism on the CPU comes from the compiler.
-* Multi-core parallelism is only supported by Conv2d(not by default).
+* Multi-core parallelism support limited.
   If the external BLAS implementation supports it,
-  there are also, gemm, gemv and ger that are parallelized.
+  many dot are parallelized via gemm, gemv and ger.
+  Also, element-wise operation are supported. See :ref:`tut_multi_cores`.
+
 * No multi-node support.
-* Many, but not all NumPy functions/aliases are implemented.
+* Most, but not all NumPy functions/aliases are implemented.
   * https://github.com/Theano/Theano/issues/1080
 * Wrapping an existing Python function in easy and documented.
 * We know how to separate the shared variable memory

doc/library/index.txt

@@ -43,7 +43,7 @@ There are also some top-level imports that you might find more convenient:
 
 .. function:: shared(...)
     
-    Alias for :func:`shared.shared`
+    Alias for :func:`theano.compile.sharedvalue.shared`
 
 .. class:: Param
 

doc/tutorial/examples.txt

@@ -276,6 +276,15 @@ expression that evaluates to a tensor of same shape and dtype.
 
 .. _using_random_numbers:
 
+
+.. note::
+
+    Theano shared variable broadcast pattern default to False for each
+    dimensions. Shared variable size can change over time, so we can't
+    use the shape to find the broadcastable pattern. If you want a
+    different pattern, just pass it as a parameter
+    ``theano.shared(..., broadcastable=(True, False))``
+
 Using Random Numbers
 ====================