Merge branch 'e-fominov-faq-changes'

docs/docs/faq.xml

@@ -329,5 +329,52 @@ cross_validate_trainer_threaded(trainer,
 
    <!-- ************************************************************************* -->
 
+   <questions group="Deep Learning">
+      <question text="Why can't I use DNN module with Visual Studio?">
+         The deep learning toolkit in dlib requires a C++11 compiler.
+         Unfortunately, as of July 2016, no versions of Visual Studio fully
+         support C++11, so not all the deep learning code will compile.
+         However, all the other modules in dlib can be used in Visual Studio
+         without any trouble.
+      </question>
+
+      <question text="Why can't I change the network architecture at runtime?">
+          A major design goal of this API is to let users create new loss
+          layers, computational layers, and solvers without needing to
+          understand or even look at the dlib internals.  A lot of the API
+          decisions are based on what makes the interface a user needs to
+          implement to create new layers as simple as possible.  In particular,
+          designing the API in this compile-time static way makes it simple for
+          these use cases.  
+          <p>
+             Here is an example of one problem it addresses.  Since dlib
+             exposes the entire network architecture to the C++ type system we
+             can get automatic serialization of networks.  Without this, we
+             would have to resort to the kind of hacky global layer registry
+             used in other tools that compose networks entirely at runtime.
+          </p>
+          <p>
+             Another nice feature is that we get to use C++11 alias template
+             statements to create network sub-blocks, which we can then use to easily
+             define very large networks.  There are examples of this in <a
+             href="dnn_introduction2_ex.cpp.html">this example program</a>.  It
+             should also be pointed out that it takes days or even weeks to
+             train one network.  So it isn't as if you will be writing a
+             program that loops over large numbers of networks and trains them
+             all.  This makes the time needed to recompile a program to change
+             the network irrelevant compared to the entire training time.
+             Moreover, there are plenty of compile time constructs in C++ you
+             can use to enumerate network architectures (e.g. loop
+             over filter widths) if you really wanted to do so.  
+          </p>
+          <p>
+             All that said, if you think you found a compelling use case that isn't supported
+             by the current API feel free to post a <a href="https://github.com/davisking/dlib/issues">github</a> issue.
+          </p>
+      </question>
+   </questions>
+
+   <!-- ************************************************************************* -->
+
 
 </doc>