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a lot of tiny changes and fixes

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Jan Grewe
2018-10-18 13:41:16 +02:00
parent 8ebf497880
commit c2dfa9ab47
4 changed files with 113 additions and 105 deletions
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4
regression/lecture/regression.tex
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@@ -383,7 +383,7 @@ introduce how this can be done without using the gradient descent
Problems that involve nonlinear computations on parameters, e.g. the
rate $\lambda$ in the exponential function $f(x;\lambda) =
\exp(\lambda x)$, do not have an analytical solution. To find minima
e^{\lambda x}$, do not have an analytical solution. To find minima
in such functions numerical methods such as the gradient descent have
to be applied.
@@ -396,7 +396,7 @@ objective functions while more specialized functions are specifically
designed for optimizations in the least square error sense
\matlabfun{lsqcurvefit()}.
\newpage
%\newpage
\begin{important}[Beware of secondary minima!]
Finding the absolute minimum is not always as easy as in the case of
the linear equation. Often, the error surface has secondary or local