added chapter about loss functions
This commit is contained in:
Clemens Dautermann
parent
a3f984996c
commit
411d967069
9 changed files with 10321 additions and 135 deletions
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\boolfalse {citerequest}\boolfalse {citetracker}\boolfalse {pagetracker}\boolfalse {backtracker}\relax
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\babel@toc {ngerman}{}
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {1}Was ist maschinelles Lernen?}{3}{section.1}%
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\contentsline {section}{\numberline {1}Was ist maschinelles Lernen?}{4}{section.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {1.1}Einsatzgebiete maschinellen Lernens}{3}{subsection.1.1}%
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\contentsline {subsection}{\numberline {1.1}Klassifizierungsprobleme}{5}{subsection.1.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {2}Neuronale Netze}{3}{section.2}%
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\contentsline {subsection}{\numberline {1.2}Regressionsprobleme}{5}{subsection.1.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {2.1}Maschinelles Lernen und menschliches Lernen}{3}{subsection.2.1}%
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\contentsline {subsection}{\numberline {1.3}Gefahren von maschinellem Lernen}{5}{subsection.1.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {2.2}Der Aufbau eines neuronalen Netzes}{4}{subsection.2.2}%
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\contentsline {subsubsection}{\numberline {1.3.1}Eignung der Datens\IeC {\"a}tze}{5}{subsubsection.1.3.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {2.3}Berechnung des Ausgabevektors}{6}{subsection.2.3}%
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\contentsline {subsubsection}{\numberline {1.3.2}Overfitting}{5}{subsubsection.1.3.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {2.4}Der Lernprozess}{8}{subsection.2.4}%
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\contentsline {subsubsection}{\numberline {1.3.3}Unbewusste Manipulation der Daten}{5}{subsubsection.1.3.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsubsection}{\numberline {2.4.1}Fehlerfunktionen}{9}{subsubsection.2.4.1}%
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\contentsline {section}{\numberline {2}Verschiedene Techniken maschinellen lernens}{5}{section.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsubsection}{\numberline {2.4.2}Gradientenverfahren}{9}{subsubsection.2.4.2}%
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\contentsline {subsection}{\numberline {2.1}\IeC {\"U}berwachtes Lernen}{5}{subsection.2.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {2.5}Verschiedene Layerarten}{9}{subsection.2.5}%
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\contentsline {subsection}{\numberline {2.2}Un\IeC {\"u}berwachtes Lernen}{5}{subsection.2.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsubsection}{\numberline {2.5.1}Fully connected Layers}{9}{subsubsection.2.5.1}%
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\contentsline {subsection}{\numberline {2.3}Best\IeC {\"a}rkendes Lernen}{5}{subsection.2.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsubsection}{\numberline {2.5.2}Convolutional Layers}{9}{subsubsection.2.5.2}%
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\contentsline {section}{\numberline {3}Neuronale Netze}{5}{section.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsubsection}{\numberline {2.5.3}Pooling Layers}{9}{subsubsection.2.5.3}%
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\contentsline {subsection}{\numberline {3.1}Maschinelles Lernen und menschliches Lernen}{5}{subsection.3.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {3}PyTorch}{9}{section.3}%
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\contentsline {subsection}{\numberline {3.2}Der Aufbau eines neuronalen Netzes}{6}{subsection.3.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {3.1}Datenvorbereitung}{9}{subsection.3.1}%
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\contentsline {subsection}{\numberline {3.3}Berechnung des Ausgabevektors}{7}{subsection.3.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {3.2}Definieren des Netzes}{9}{subsection.3.2}%
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\contentsline {subsection}{\numberline {3.4}Der Lernprozess}{9}{subsection.3.4}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {3.3}Trainieren des Netzes}{9}{subsection.3.3}%
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\contentsline {subsection}{\numberline {3.5}Fehlerfunktionen}{10}{subsection.3.5}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {4}Fallbeispiel I:\newline Ein Klassifizierungsnetzwerk f\IeC {\"u}r handgeschriebene Ziffern}{9}{section.4}%
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\contentsline {subsubsection}{\numberline {3.5.1}MSE -- Durchschnittlicher quadratischer Fehler}{10}{subsubsection.3.5.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {4.1}Aufgabe}{9}{subsection.4.1}%
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\contentsline {subsubsection}{\numberline {3.5.2}MAE -- Durchschnitztlicher absoluter Fehler}{10}{subsubsection.3.5.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {4.2}Der MNIST Datensatz}{9}{subsection.4.2}%
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\contentsline {subsubsection}{\numberline {3.5.3}Kreuzentropiefehler}{11}{subsubsection.3.5.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {4.3}Fragmentbasierte Erkennung}{9}{subsection.4.3}%
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\contentsline {subsection}{\numberline {3.6}Gradientenverfahren}{13}{subsection.3.6}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {4.4}Ergebnis}{9}{subsection.4.4}%
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\contentsline {subsection}{\numberline {3.7}Verschiedene Layerarten}{13}{subsection.3.7}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {5}Fallbeispiel II:\newline Eine selbsttrainierende KI f\IeC {\"u}r Tic-Tac-Toe}{9}{section.5}%
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\contentsline {subsubsection}{\numberline {3.7.1}Fully connected Layers}{13}{subsubsection.3.7.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.1}Das Prinzip}{9}{subsection.5.1}%
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\contentsline {subsubsection}{\numberline {3.7.2}Convolutional Layers}{13}{subsubsection.3.7.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.2}Chance-Tree Optimierung}{9}{subsection.5.2}%
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\contentsline {subsubsection}{\numberline {3.7.3}Pooling Layers}{13}{subsubsection.3.7.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.3}L\IeC {\"o}sung mittels eines neuronalen Netzes}{9}{subsection.5.3}%
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\contentsline {section}{\numberline {4}PyTorch}{13}{section.4}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.4}Vergleich}{9}{subsection.5.4}%
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\contentsline {subsection}{\numberline {4.1}Datenvorbereitung}{13}{subsection.4.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {6}Schlusswort}{9}{section.6}%
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\contentsline {subsection}{\numberline {4.2}Definieren des Netzes}{13}{subsection.4.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {4.3}Trainieren des Netzes}{13}{subsection.4.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {5}Fallbeispiel I:\newline Ein Klassifizierungsnetzwerk f\IeC {\"u}r handgeschriebene Ziffern}{13}{section.5}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.1}Aufgabe}{13}{subsection.5.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.2}Der MNIST Datensatz}{13}{subsection.5.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.3}Fragmentbasierte Erkennung}{13}{subsection.5.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {5.4}Ergebnis}{13}{subsection.5.4}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {6}Fallbeispiel II:\newline Eine selbsttrainierende KI f\IeC {\"u}r Tic-Tac-Toe}{13}{section.6}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {6.1}Das Prinzip}{13}{subsection.6.1}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {6.2}Chance-Tree Optimierung}{13}{subsection.6.2}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {6.3}L\IeC {\"o}sung mittels eines neuronalen Netzes}{13}{subsection.6.3}%
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\defcounter {refsection}{0}\relax
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\contentsline {subsection}{\numberline {6.4}Vergleich}{13}{subsection.6.4}%
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\defcounter {refsection}{0}\relax
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\contentsline {section}{\numberline {7}Schlusswort}{13}{section.7}%
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