\BOOKMARK [1][-]{section.1}{Was ist maschinelles Lernen?}{}% 1
\BOOKMARK [2][-]{subsection.1.1}{Einsatzgebiete maschinellen Lernens}{section.1}% 2
\BOOKMARK [1][-]{section.2}{Neuronale Netze}{}% 3
\BOOKMARK [2][-]{subsection.2.1}{Maschinelles Lernen und menschliches Lernen}{section.2}% 4
\BOOKMARK [2][-]{subsection.2.2}{Der Aufbau eines neuronalen Netzes}{section.2}% 5
\BOOKMARK [2][-]{subsection.2.3}{Berechnung des Ausgabevektors}{section.2}% 6
\BOOKMARK [2][-]{subsection.2.4}{Der Lernprozess}{section.2}% 7
\BOOKMARK [3][-]{subsubsection.2.4.1}{Fehlerfunktionen}{subsection.2.4}% 8
\BOOKMARK [3][-]{subsubsection.2.4.2}{Gradientenverfahren}{subsection.2.4}% 9
\BOOKMARK [2][-]{subsection.2.5}{Verschiedene Layerarten}{section.2}% 10
\BOOKMARK [3][-]{subsubsection.2.5.1}{Fully connected Layers}{subsection.2.5}% 11
\BOOKMARK [3][-]{subsubsection.2.5.2}{Convolutional Layers}{subsection.2.5}% 12
\BOOKMARK [3][-]{subsubsection.2.5.3}{Pooling Layers}{subsection.2.5}% 13
\BOOKMARK [1][-]{section.3}{PyTorch}{}% 14
\BOOKMARK [2][-]{subsection.3.1}{Datenvorbereitung}{section.3}% 15
\BOOKMARK [2][-]{subsection.3.2}{Definieren des Netzes}{section.3}% 16
\BOOKMARK [2][-]{subsection.3.3}{Trainieren des Netzes}{section.3}% 17
\BOOKMARK [1][-]{section.4}{Fallbeispiel I:Ein Klassifizierungsnetzwerk f\374r handgeschriebene Ziffern}{}% 18
\BOOKMARK [2][-]{subsection.4.1}{Aufgabe}{section.4}% 19
\BOOKMARK [2][-]{subsection.4.2}{Der MNIST Datensatz}{section.4}% 20
\BOOKMARK [2][-]{subsection.4.3}{Fragmentbasierte Erkennung}{section.4}% 21
\BOOKMARK [2][-]{subsection.4.4}{Ergebnis}{section.4}% 22
\BOOKMARK [1][-]{section.5}{Fallbeispiel II:Eine selbsttrainierende KI f\374r Tic-Tac-Toe}{}% 23
\BOOKMARK [2][-]{subsection.5.1}{Das Prinzip}{section.5}% 24
\BOOKMARK [2][-]{subsection.5.2}{Chance-Tree Optimierung}{section.5}% 25
\BOOKMARK [2][-]{subsection.5.3}{L\366sung mittels eines neuronalen Netzes}{section.5}% 26
\BOOKMARK [2][-]{subsection.5.4}{Vergleich}{section.5}% 27
\BOOKMARK [1][-]{section.6}{Schlusswort}{}% 28