give a little bit more info about theano gradient in the doc.

doc/index.txt

@@ -8,7 +8,7 @@ arrays efficiently. Theano features:
 
 * **tight integration with numpy** -- Use `numpy.ndarray` in Theano-compiled functions.
 * **transparent use of a GPU** -- Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
-* **symbolic differentiation** -- Let Theano do your derivatives.
+* **efficient symbolic differentiation** -- Theano does your derivatives for function with one or many inputs.
 * **speed and stability optimizations** -- Get the right answer for ``log(1+x)`` even when ``x`` is really tiny.
 * **dynamic C code generation** -- Evaluate expressions faster.
 * **extensive unit-testing and self-verification** -- Detect and diagnose many types of mistake.

doc/tutorial/examples.txt

@@ -144,6 +144,8 @@ array([[ 0.25      ,  0.19661193],
 The resulting function computes the gradient of its first argument
 with respect to the second. In this way, Theano can be used for
 `automatic differentiation <http://en.wikipedia.org/wiki/Automatic_differentiation>`_.
+As opposed to what this page tell, Theano do efficient symbolic differentiation 
+even for function with many inputs.
 
 .. note::