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Finished paper roguely

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Clemens Dautermann 2020-02-04 23:18:33 +01:00
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doc/Grundlagen_des_maschinellen_lernens.out
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\BOOKMARK [2][-]{subsection.4.2}{Definieren des Netzes}{section.4}% 27
\BOOKMARK [2][-]{subsection.4.3}{Trainieren des Netzes}{section.4}% 28
\BOOKMARK [2][-]{subsection.4.4}{Pytorch und weights and biases}{section.4}% 29
\BOOKMARK [1][-]{section.5}{Fallbeispiel I:Ein Klassifizierungsnetzwerk f\374r handgeschriebene Ziffern}{}% 30
\BOOKMARK [1][-]{section.5}{Ein Klassifizierungsnetzwerk f\374r handgeschriebene Ziffern}{}% 30
\BOOKMARK [2][-]{subsection.5.1}{Aufgabe}{section.5}% 31
\BOOKMARK [2][-]{subsection.5.2}{Der MNIST Datensatz}{section.5}% 32
\BOOKMARK [2][-]{subsection.5.3}{Das Netz}{section.5}% 33
\BOOKMARK [2][-]{subsection.5.4}{Ergebnis}{section.5}% 34
\BOOKMARK [1][-]{section.6}{Fallbeispiel II:Eine selbsttrainierende KI f\374r Tic-Tac-Toe}{}% 35
\BOOKMARK [2][-]{subsection.6.1}{Das Prinzip}{section.6}% 36
\BOOKMARK [2][-]{subsection.6.2}{Chance-Tree Optimierung}{section.6}% 37
\BOOKMARK [2][-]{subsection.6.3}{L\366sung mittels eines neuronalen Netzes}{section.6}% 38
\BOOKMARK [2][-]{subsection.6.4}{Vergleich}{section.6}% 39
\BOOKMARK [1][-]{section.7}{Schlusswort}{}% 40
\BOOKMARK [1][-]{section.6}{Schlusswort}{}% 35