pytorch-ai/doc/Grundlagen_des_maschinellen_lernens.toc
2020-01-17 23:28:10 +01:00

82 lines
5.2 KiB
TeX

\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax
\babel@toc {ngerman}{}
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {1}Was ist maschinelles Lernen?}{3}{section.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {1.1}Klassifizierungsprobleme}{4}{subsection.1.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {1.2}Regressionsprobleme}{4}{subsection.1.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {1.3}Gefahren von maschinellem Lernen}{4}{subsection.1.3}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {1.3.1}Eignung der Datens\IeC {\"a}tze}{4}{subsubsection.1.3.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {1.3.2}Overfitting}{4}{subsubsection.1.3.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {1.3.3}Unbewusste Manipulation der Daten}{4}{subsubsection.1.3.3}%
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {2}Verschiedene Techniken maschinellen lernens}{4}{section.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {2.1}\IeC {\"U}berwachtes Lernen}{4}{subsection.2.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {2.2}Un\IeC {\"u}berwachtes Lernen}{4}{subsection.2.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {2.3}Best\IeC {\"a}rkendes Lernen}{4}{subsection.2.3}%
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {3}Neuronale Netze}{4}{section.3}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.1}Maschinelles Lernen und menschliches Lernen}{4}{subsection.3.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.2}Der Aufbau eines neuronalen Netzes}{5}{subsection.3.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.3}Berechnung des Ausgabevektors}{6}{subsection.3.3}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.4}Der Lernprozess}{8}{subsection.3.4}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.5}Fehlerfunktionen}{9}{subsection.3.5}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {3.5.1}MSE -- Durchschnittlicher quadratischer Fehler}{9}{subsubsection.3.5.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {3.5.2}MAE -- Durchschnitztlicher absoluter Fehler}{9}{subsubsection.3.5.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {3.5.3}Kreuzentropiefehler}{10}{subsubsection.3.5.3}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.6}Gradientenverfahren und Backpropagation}{11}{subsection.3.6}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {3.6.1}Lernrate}{12}{subsubsection.3.6.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {3.7}Verschiedene Layerarten}{13}{subsection.3.7}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {3.7.1}Convolutional Layers}{13}{subsubsection.3.7.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{\numberline {3.7.2}Pooling Layers}{15}{subsubsection.3.7.2}%
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {4}PyTorch}{18}{section.4}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {4.1}Datenvorbereitung}{18}{subsection.4.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {4.2}Definieren des Netzes}{18}{subsection.4.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {4.3}Trainieren des Netzes}{18}{subsection.4.3}%
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {5}Fallbeispiel I:\newline Ein Klassifizierungsnetzwerk f\IeC {\"u}r handgeschriebene Ziffern}{18}{section.5}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {5.1}Aufgabe}{18}{subsection.5.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {5.2}Der MNIST Datensatz}{18}{subsection.5.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {5.3}Fragmentbasierte Erkennung}{18}{subsection.5.3}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {5.4}Ergebnis}{18}{subsection.5.4}%
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {6}Fallbeispiel II:\newline Eine selbsttrainierende KI f\IeC {\"u}r Tic-Tac-Toe}{18}{section.6}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {6.1}Das Prinzip}{18}{subsection.6.1}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {6.2}Chance-Tree Optimierung}{18}{subsection.6.2}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {6.3}L\IeC {\"o}sung mittels eines neuronalen Netzes}{18}{subsection.6.3}%
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {6.4}Vergleich}{18}{subsection.6.4}%
\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {7}Schlusswort}{18}{section.7}%