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Added chapters, trained net

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Clemens Dautermann 2019-12-27 20:08:35 +01:00
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doc/Grundlagen_des_maschinellen_lernens.lof
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\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax
\babel@toc {ngerman}{}
\defcounter {refsection}{0}\relax
\contentsline {figure}{\numberline {1}{\ignorespaces Neuron \newline Quelle: simple.wikipedia.org/wiki/File:Neuron.svg\newline Copyright: CC Attribution-Share Alike von Nutzer Dhp1080,\newline bearbeitet}}{4}{figure.1}
\contentsline {figure}{\numberline {1}{\ignorespaces Neuron \newline Quelle: simple.wikipedia.org/wiki/File:Neuron.svg\newline Copyright: CC Attribution-Share Alike von Nutzer Dhp1080,\newline bearbeitet}}{4}{figure.1}%
\defcounter {refsection}{0}\relax
\contentsline {figure}{\numberline {2}{\ignorespaces Ein einfaches neuronales Netz}}{5}{figure.2}
\contentsline {figure}{\numberline {2}{\ignorespaces Ein einfaches neuronales Netz}}{5}{figure.2}%
\defcounter {refsection}{0}\relax
\contentsline {figure}{\numberline {3}{\ignorespaces Der Plot der Sigmoid Funktion $\sigma (x)=\frac {e^x}{e^x+1}$}}{6}{figure.3}%
\defcounter {refsection}{0}\relax
\contentsline {figure}{\numberline {4}{\ignorespaces Formel zur Berechnung eines Ausgabevektors aus einem Eingabevektor durch ein Layer Neuronen. }}{7}{figure.4}%