[script] solutions of exercises at end of chapters

regression/lecture/regression.tex

@@ -1,6 +1,5 @@
 \chapter{\tr{Optimization and gradient descent}{Optimierung und Gradientenabstieg}}
 
-% \selectlanguage{ngerman}
 To understand the behaviour of a given system sciences often probe the
 system with input signals and then try to explain the responses
 through a model. Typically the model has a few parameter that specify
@@ -356,7 +355,6 @@ large.
     iteration of the algorithm.} \label{gradientdescentfig}
 \end{figure}
 
-\setboolean{showexercisesolutions}{false}
 \begin{exercise}{gradientDescent.m}{}
   Implement the gradient descent for the problem of the linear
   equation for the measured data in file \file{lin\_regression.mat}.
@@ -407,4 +405,6 @@ designed for optimizations in the least square error sense
   parameter increases complexity and is computationally expensive.
 \end{important}
 
-\selectlanguage{english}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\printsolutions

regression/lecture/regression_de.tex

@@ -368,7 +368,6 @@ Punkte in Abbildung \ref{gradientdescentfig} gro{\ss}.
     Optimierungsschritt an.} \label{gradientdescentfig}
 \end{figure}
 
-\setboolean{showexercisesolutions}{false}
 \begin{exercise}{gradientDescent.m}{}
   Implementiere den Gradientenabstieg f\"ur das Problem der
   Parameteranpassung der linearen Geradengleichung an die Messdaten in