big pass over documentation to make titles consistent across all packages..

2 files changed, 20 insertions, 20 deletions

diff --git a/doklua/index.dok b/doklua/index.dok
index 6baad95..8728c6b 100644
--- a/doklua/index.dok
+++ b/doklua/index.dok
@@ -13,7 +13,7 @@ information, or have a look on the [[LuaManual|Lua Reference Manual]].
 
 =====  Why choose Lua? =====
 
-===  Lua is a proven and robust language ===
+====  Lua is a proven and robust language ====
 
  Lua has been used in
 [[http://www.lua.org/uses.html|many industrial applications]] (e.g.,
@@ -25,7 +25,7 @@ is currently the leading scripting language in games. Lua has a solid
 [[http://www.lua.org/versions.html|versions]] of Lua have been released
 and used in real applications since its creation in 1993.
 
-===  Lua is fast  ===
+====  Lua is fast  ====
 
 Lua has a deserved reputation for performance. To
 claim to be "as fast as Lua" is an aspiration of other scripting
@@ -34,7 +34,7 @@ of interpreted scripting languages. Lua is fast not only in fine-tuned
 benchmark programs, but in real life too. A substantial fraction of large
 applications have been written in Lua.
 
-===  Lua is portable  ===
+====  Lua is portable  ====
 
 Lua is [[http://www.lua.org/download.html|distributed]] in a small
 package that builds out-of-the-box in all platforms that have an ''ANSI/ISO C''
@@ -43,7 +43,7 @@ devices (such as handheld computers and cell phones that use ''BREW'', ''Symbian
 ''Pocket PC'', etc.) and embedded microprocessors (such as ''ARM'' and ''Rabbit'') for
 applications like ''Lego MindStorms''.
 
-===  Lua is embeddable  === 
+====  Lua is embeddable  ==== 
 
 Lua is a fast language engine with small footprint that you can embed into
 your application. Lua has a simple and well documented ''API'' that allows
@@ -55,7 +55,7 @@ extend programs written not only in ''C'' and ''C++'', but also in ''Java'', ''C
 such as
 ''Perl'' and ''Ruby''.
 
-===  Lua is simple and powerful  === 
+====  Lua is simple and powerful  ==== 
 
 A fundamental concept in the design of Lua is to provide //meta-mechanisms//
 for implementing features, instead of providing a host of features directly
@@ -65,14 +65,14 @@ inheritance. Lua's meta-mechanisms bring an economy of concepts and keep
 the language small, while allowing the semantics to be extended in
 unconventional ways.
 
-===  Lua is free  ===
+====  Lua is free  ====
 
 Lua is free software, distributed under a
 [[http://www.lua.org/license.html|liberal license]] (the well-known ''MIT''
 license). It can be used for both academic and commercial purposes at
 absolutely no cost. Just [[http://www.lua.org/download.html|download]] it and use it.
 
-=====  Where does Lua come from? =====
+====  Where does Lua come from? ====
 
 Lua is designed and implemented by a team at
 [[http://www.puc-rio.br/|PUC-Rio]], the Pontifical Catholic University of
diff --git a/doktutorial/index.dok b/doktutorial/index.dok
index a11a95e..b424a14 100644
--- a/doktutorial/index.dok
+++ b/doktutorial/index.dok
@@ -10,7 +10,7 @@ vectors, matrices and tensors and how to build and train a basic
 neural network. For anything else, you should know how to access the
 html help and read about how to do it.
 
-======  What is Torch? ======
+=====  What is Torch? =====
 
 Torch7 provides a Matlab-like environment for state-of-the-art machine
 learning algorithms. It is easy to use and provides a very efficient
@@ -18,14 +18,14 @@ implementation, thanks to a easy and fast scripting language (Lua) and
 an underlying C/C++ implementation.  You can read more about Lua
 [[http://www.lua.org|here]].
 
-======  Installation ======
+=====  Installation =====
 
 First before you can do anything, you need to install Torch7 on your
 machine.  That is not described in detail here, but is instead
 described in the [[..:install:index|installation help]].
 
 
-======  Checking your installation works and requiring packages ======
+=====  Checking your installation works and requiring packages =====
 
 If you have got this far, hopefully your Torch installation works. A simple
 way to make sure it does is to start Lua from the shell command line, 
@@ -55,7 +55,7 @@ or higher-dimensional objects (tensors).
 Tensors). To see the list of all packages distributed with Torch7,
 click [[..:index|here]].
 
-====== Getting Help ======
+===== Getting Help =====
 
 There are two main ways of getting help in Torch7. One way is ofcourse
 the html formatted help. However, another and easier method is to use
@@ -100,7 +100,7 @@ t7> torch.randn(
 
 </file>
 
-======  Torch Basics: Playing with Tensors ======
+=====  Torch Basics: Playing with Tensors =====
 
 Ok, now we are ready to actually do something in Torch.  Lets start by
 constructing a vector, say a vector with 5 elements, and filling the
@@ -229,7 +229,7 @@ t7> =torch.mm(a,b)
 
 </file>
 
-====== Types in Torch7 ======
+===== Types in Torch7 =====
 
 In Torch7, different types of tensors can be used. By default, all
 tensors are created using ''double'' type. ''torch.Tensor'' is a
@@ -244,12 +244,12 @@ t7> =torch.Tensor()
 [torch.FloatTensor with no dimension]
 </file>
 
-======  Example: training a neural network ======
+=====  Example: training a neural network =====
 
 We will show now how to train a neural network using the [[..:nn:index|nn]] package
 available in Torch.
 
-=====  Torch basics: building a dataset using Lua tables =====
+====  Torch basics: building a dataset using Lua tables ====
 
 In general the user has the freedom to create any kind of structure he
 wants for dealing with data.
@@ -302,7 +302,7 @@ for i=1,dataset:size() do
 end
 </file>
 
-=====  Torch basics: building a neural network =====
+====  Torch basics: building a neural network ====
 
 To train a neural network we first need some data.  We can use the XOR data
 we just generated in the section before.  Now all that remains is to define
@@ -336,7 +336,7 @@ mlp:add(nn.Linear(HUs,outputs))
 </file>
 
 
-=====  Torch basics: training a neural network =====
+====  Torch basics: training a neural network ====
 
 Now we're ready to train.
 This is done with the following code:
@@ -373,7 +373,7 @@ See the nn package description of the
 for more details.
 
 
-=====  Torch basics: testing your neural network =====
+====  Torch basics: testing your neural network ====
 
 To test your network on a single example you can do this:
 <file lua>
@@ -402,7 +402,7 @@ t7> x=torch.Tensor(2); x[1]=-0.5; x[2]=-0.5; print(mlp:forward(x))
 [torch.DoubleTensor of dimension 1]
 </file>
 
-=====  Manual Training of a Neural Network =====
+====  Manual Training of a Neural Network ====
 
 Instead of using the [[..:nn:index#nn.StochasticGradient|StochasticGradient]] class
 you can directly make the forward and backward calls on the network yourself.
@@ -448,7 +448,7 @@ end
 
 Super!
 
-====== Concluding remarks ======
+===== Concluding remarks =====
 
 That's the end of this tutorial, but not the end of what you have left
 to discover of Torch! To explore more of Torch, you should take a look