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completed gradient descent section

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Clemens Dautermann 2020-01-11 21:01:35 +01:00
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doc/Grundlagen_des_maschinellen_lernens.out
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\BOOKMARK [3][-]{subsubsection.3.5.2}{MAE \205 Durchschnitztlicher absoluter Fehler}{subsection.3.5}% 19
\BOOKMARK [3][-]{subsubsection.3.5.3}{Kreuzentropiefehler}{subsection.3.5}% 20
\BOOKMARK [2][-]{subsection.3.6}{Gradientenverfahren und Backpropagation}{section.3}% 21
\BOOKMARK [2][-]{subsection.3.7}{Verschiedene Layerarten}{section.3}% 22
\BOOKMARK [3][-]{subsubsection.3.7.1}{Fully connected Layers}{subsection.3.7}% 23
\BOOKMARK [3][-]{subsubsection.3.7.2}{Convolutional Layers}{subsection.3.7}% 24
\BOOKMARK [3][-]{subsubsection.3.7.3}{Pooling Layers}{subsection.3.7}% 25
\BOOKMARK [1][-]{section.4}{PyTorch}{}% 26
\BOOKMARK [2][-]{subsection.4.1}{Datenvorbereitung}{section.4}% 27
\BOOKMARK [2][-]{subsection.4.2}{Definieren des Netzes}{section.4}% 28
\BOOKMARK [2][-]{subsection.4.3}{Trainieren des Netzes}{section.4}% 29
\BOOKMARK [1][-]{section.5}{Fallbeispiel I: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}{Fragmentbasierte Erkennung}{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 [3][-]{subsubsection.3.6.1}{Lernrate}{subsection.3.6}% 22
\BOOKMARK [2][-]{subsection.3.7}{Verschiedene Layerarten}{section.3}% 23
\BOOKMARK [3][-]{subsubsection.3.7.1}{Fully connected Layers}{subsection.3.7}% 24
\BOOKMARK [3][-]{subsubsection.3.7.2}{Convolutional Layers}{subsection.3.7}% 25
\BOOKMARK [3][-]{subsubsection.3.7.3}{Pooling Layers}{subsection.3.7}% 26
\BOOKMARK [1][-]{section.4}{PyTorch}{}% 27
\BOOKMARK [2][-]{subsection.4.1}{Datenvorbereitung}{section.4}% 28
\BOOKMARK [2][-]{subsection.4.2}{Definieren des Netzes}{section.4}% 29
\BOOKMARK [2][-]{subsection.4.3}{Trainieren des Netzes}{section.4}% 30
\BOOKMARK [1][-]{section.5}{Fallbeispiel I:Ein Klassifizierungsnetzwerk f\374r handgeschriebene Ziffern}{}% 31
\BOOKMARK [2][-]{subsection.5.1}{Aufgabe}{section.5}% 32
\BOOKMARK [2][-]{subsection.5.2}{Der MNIST Datensatz}{section.5}% 33
\BOOKMARK [2][-]{subsection.5.3}{Fragmentbasierte Erkennung}{section.5}% 34
\BOOKMARK [2][-]{subsection.5.4}{Ergebnis}{section.5}% 35
\BOOKMARK [1][-]{section.6}{Fallbeispiel II:Eine selbsttrainierende KI f\374r Tic-Tac-Toe}{}% 36
\BOOKMARK [2][-]{subsection.6.1}{Das Prinzip}{section.6}% 37
\BOOKMARK [2][-]{subsection.6.2}{Chance-Tree Optimierung}{section.6}% 38
\BOOKMARK [2][-]{subsection.6.3}{L\366sung mittels eines neuronalen Netzes}{section.6}% 39
\BOOKMARK [2][-]{subsection.6.4}{Vergleich}{section.6}% 40
\BOOKMARK [1][-]{section.7}{Schlusswort}{}% 41