Merge pull request #999 from nouiz/doc

doc/internal/metadocumentation.txt

@@ -39,7 +39,7 @@ For more details:
 Use ReST for documentation
 --------------------------
 
- * :ref:`ReST <http://docutils.sourceforge.net/rst.html>` is standardized.
+ * `ReST <http://docutils.sourceforge.net/rst.html>`__ is standardized.
    epydoc is not. trac wiki-markup is not.
    This means that ReST can be cut-and-pasted between epydoc, code, other
    docs, and TRAC.  This is a huge win!

doc/library/gof/index.txt

@@ -13,7 +13,7 @@
 .. toctree::
     :maxdepth: 1
 
-    fg
+    fgraph
     toolbox
     type
 

doc/library/tensor/nnet/conv.txt

@@ -7,8 +7,8 @@
 .. note::
 
     Two similar implementation exists for conv2d:
-    **theano.tensor.signal.conv.conv2d** and
-    **theano.tensor.nnet.conv.conv2d**. The foremer implements a traditional
+    :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
+    :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>. The former implements a traditional
     2D convolution, while the latter implements the convolutional layers
     present in convolutional neural networks (where filters are 3D and pool
     over several input channels).
@@ -26,6 +26,5 @@ TODO: Give examples for how to use these things! They are pretty complicated.
     - :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.
     - :func:`conv3D <theano.tensor.nnet.Conv3D.conv3D>`.
 
-.. autofunction:: theano.tensor.signal.conv.conv2d
 .. autofunction:: theano.tensor.nnet.conv.conv2d
 .. autofunction:: theano.tensor.nnet.Conv3D.conv3D

doc/proposals/complex_gradient.txt

@@ -52,9 +52,9 @@ variables, and then:
     \frac{\partial C}{\partial r} = \frac{\partial C}{\partial x} \frac{\partial x}{\partial r} + \frac{\partial C}{\partial y} \frac{\partial y}{\partial r}
 
 If we want to use an algorithm similar to gradient backpropagation,
-we can see that, here, we need to have both :math:\frac{\partial
-C}{\partial \Re t} and :math:\frac{\partial C}{\partial \Im t}, in order
-to compute :math:`\frac{\partial C}{\partial r}.
+we can see that, here, we need to have both :math:`\frac{\partial
+C}{\partial \Re t}` and :math:`\frac{\partial C}{\partial \Im t}`, in order
+to compute :math:`\frac{\partial C}{\partial r}`.
 
 For each variable :math:`v` in the expression graph, let us denote
 :math:`\nabla_C(v)` the *gradient* of :math:`C` with respect to