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Clemens Dautermann 2020-01-23 16:38:18 +01:00
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doc/Grundlagen_des_maschinellen_lernens.out
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\BOOKMARK [2][-]{subsection.1.1}{Klassifizierungsprobleme}{section.1}% 2
\BOOKMARK [2][-]{subsection.1.2}{Regressionsprobleme}{section.1}% 3
\BOOKMARK [2][-]{subsection.1.3}{Gefahren von maschinellem Lernen}{section.1}% 4
\BOOKMARK [3][-]{subsubsection.1.3.1}{Eignung der Datens\344tze}{subsection.1.3}% 5
\BOOKMARK [3][-]{subsubsection.1.3.2}{Overfitting}{subsection.1.3}% 6
\BOOKMARK [3][-]{subsubsection.1.3.3}{Unbewusste Manipulation der Daten}{subsection.1.3}% 7
\BOOKMARK [1][-]{section.2}{Verschiedene Techniken maschinellen lernens}{}% 8
\BOOKMARK [2][-]{subsection.2.1}{\334berwachtes Lernen}{section.2}% 9
\BOOKMARK [2][-]{subsection.2.2}{Un\374berwachtes Lernen}{section.2}% 10
\BOOKMARK [2][-]{subsection.2.3}{Best\344rkendes Lernen}{section.2}% 11
\BOOKMARK [1][-]{section.3}{Neuronale Netze}{}% 12
\BOOKMARK [2][-]{subsection.3.1}{Maschinelles Lernen und menschliches Lernen}{section.3}% 13
\BOOKMARK [2][-]{subsection.3.2}{Der Aufbau eines neuronalen Netzes}{section.3}% 14
\BOOKMARK [2][-]{subsection.3.3}{Berechnung des Ausgabevektors}{section.3}% 15
\BOOKMARK [2][-]{subsection.3.4}{Der Lernprozess}{section.3}% 16
\BOOKMARK [2][-]{subsection.3.5}{Fehlerfunktionen}{section.3}% 17
\BOOKMARK [3][-]{subsubsection.3.5.1}{MSE \205 Durchschnittlicher quadratischer Fehler}{subsection.3.5}% 18
\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 [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}{Convolutional Layers}{subsection.3.7}% 24
\BOOKMARK [3][-]{subsubsection.3.7.2}{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.1.3.1}{Overfitting}{subsection.1.3}% 5
\BOOKMARK [3][-]{subsubsection.1.3.2}{Die Daten}{subsection.1.3}% 6
\BOOKMARK [1][-]{section.2}{Verschiedene Techniken maschinellen Lernens}{}% 7
\BOOKMARK [2][-]{subsection.2.1}{\334berwachtes Lernen}{section.2}% 8
\BOOKMARK [2][-]{subsection.2.2}{Un\374berwachtes Lernen}{section.2}% 9
\BOOKMARK [2][-]{subsection.2.3}{Best\344rkendes Lernen}{section.2}% 10
\BOOKMARK [1][-]{section.3}{Neuronale Netze}{}% 11
\BOOKMARK [2][-]{subsection.3.1}{Maschinelles Lernen und menschliches Lernen}{section.3}% 12
\BOOKMARK [2][-]{subsection.3.2}{Der Aufbau eines neuronalen Netzes}{section.3}% 13
\BOOKMARK [2][-]{subsection.3.3}{Berechnung des Ausgabevektors}{section.3}% 14
\BOOKMARK [2][-]{subsection.3.4}{Der Lernprozess}{section.3}% 15
\BOOKMARK [2][-]{subsection.3.5}{Fehlerfunktionen}{section.3}% 16
\BOOKMARK [3][-]{subsubsection.3.5.1}{MSE \205 Durchschnittlicher quadratischer Fehler}{subsection.3.5}% 17
\BOOKMARK [3][-]{subsubsection.3.5.2}{MAE \205 Durchschnitztlicher absoluter Fehler}{subsection.3.5}% 18
\BOOKMARK [3][-]{subsubsection.3.5.3}{Kreuzentropiefehler}{subsection.3.5}% 19
\BOOKMARK [2][-]{subsection.3.6}{Gradientenverfahren und Backpropagation}{section.3}% 20
\BOOKMARK [3][-]{subsubsection.3.6.1}{Lernrate}{subsection.3.6}% 21
\BOOKMARK [2][-]{subsection.3.7}{Verschiedene Layerarten}{section.3}% 22
\BOOKMARK [3][-]{subsubsection.3.7.1}{Convolutional Layers}{subsection.3.7}% 23
\BOOKMARK [3][-]{subsubsection.3.7.2}{Pooling Layers}{subsection.3.7}% 24
\BOOKMARK [1][-]{section.4}{PyTorch}{}% 25
\BOOKMARK [2][-]{subsection.4.1}{Datenvorbereitung}{section.4}% 26
\BOOKMARK [2][-]{subsection.4.2}{Definieren des Netzes}{section.4}% 27
\BOOKMARK [2][-]{subsection.4.3}{Trainieren des Netzes}{section.4}% 28
\BOOKMARK [1][-]{section.5}{Fallbeispiel I:Ein Klassifizierungsnetzwerk f\374r handgeschriebene Ziffern}{}% 29
\BOOKMARK [2][-]{subsection.5.1}{Aufgabe}{section.5}% 30
\BOOKMARK [2][-]{subsection.5.2}{Der MNIST Datensatz}{section.5}% 31
\BOOKMARK [2][-]{subsection.5.3}{Fragmentbasierte Erkennung}{section.5}% 32
\BOOKMARK [2][-]{subsection.5.4}{Ergebnis}{section.5}% 33
\BOOKMARK [1][-]{section.6}{Fallbeispiel II:Eine selbsttrainierende KI f\374r Tic-Tac-Toe}{}% 34
\BOOKMARK [2][-]{subsection.6.1}{Das Prinzip}{section.6}% 35
\BOOKMARK [2][-]{subsection.6.2}{Chance-Tree Optimierung}{section.6}% 36
\BOOKMARK [2][-]{subsection.6.3}{L\366sung mittels eines neuronalen Netzes}{section.6}% 37
\BOOKMARK [2][-]{subsection.6.4}{Vergleich}{section.6}% 38
\BOOKMARK [1][-]{section.7}{Schlusswort}{}% 39