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15
Appendix.tex
15
Appendix.tex
@ -1,18 +1,3 @@
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\pagenumbering{roman}
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%glossary
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\printglossary[nonumberlist]
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\newpage
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%bibliography
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\phantomsection
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\addcontentsline{toc}{chapter}{Literatur}
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\bibliographystyle{IEEEtran-de}
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\bibliography{Bibliography.bib}
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\newpage
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%appendix
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\appendix
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\chapter{Anhang}
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21
Glossary.tex
21
Glossary.tex
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\newglossary{nomenclature}{nom}{ncl}{Nomenklatur}
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\shorthandon{"}
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%--------------------
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%main glossary
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%--------------------
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% {{{ Main glossary%
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\newglossaryentry{overfitting}{
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name=Overfitting,
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description={
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beeinflussen sich dabei aber nicht.} (\url{https://de.wikipedia.org/wiki/Unabh\%C3\%A4ngig_und_identisch_verteilte_Zufallsvariablen})
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}
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}
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% }}} %
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%--------------------
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%acronyms
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%--------------------
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% {{{ acronyms%
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\setabbreviationstyle[acronym]{long-short}
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\newacronym{CNN}{CNN}{Convolutional Neural Network}
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\newacronym{RNN}{RNN}{Recurrent Neural Network}
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\newacronym{SSE}{SSE}{Summed Squared Error}
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\newacronym{MSE}{MSE}{Mean Squared Error}
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\newacronym{FRM}{FRM}{\gls{full_rank_matrix}}
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@ -84,10 +83,11 @@
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\newacronym{GPU}{GPU}{Graphic Processing Unit}
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\newacronym{RMS}{RMS}{Root Mean Square}
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%--------------------
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%nomenclature
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%--------------------
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% }}} %
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% {{{ Nomenclature%
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% {{{ Nomencalture Commands %
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%add new key
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%\glsaddstoragekey{unit}{}{\glsentryunit}
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\glsnoexpandfields
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\newcommand{\nomsym}[1]{\texorpdfstring{\glslink{#1}{\ensuremath{\glsentrysymbol{#1}}}}{#1}\xspace}
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%use nomenclature entry (use in equation)
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\newcommand{\nomeq}[1]{\glslink{#1}{\glsentrysymbol{#1}}}
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% }}} Nomencalture Commands %
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\newnom{summed_squared_error}{\gls{SSE}}{\text{\glsxtrshort{SSE}}}{\glsxtrfull{SSE}}
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\newnom{mean_squared_error}{\gls{MSE}}{\text{\glsxtrshort{MSE}}}{\glsxtrfull{MSE}}
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@ -138,6 +139,8 @@
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\newnom{gaussian_process}{Gaußscher Prozess}{\mathcal{GP}}{}
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\newnom{hyper_parameters}{Hyper"~Parameter}{\bm{\beta}}{}
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\newnom{activation_function}{Aktivierungsfunktion}{\phi}{}
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% }}} %
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\shorthandoff{"}
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\makeglossaries
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\def \MODULECOMPACT{ML}
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\def \DATE{\today}
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\includeonly{
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%Einleitung
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chapters/Einleitung,
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%Classical_Supervised_Learning
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chapters/Classical_Supervised_Learning/Linear_Regression,
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chapters/Classical_Supervised_Learning/Linear_Classification,
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chapters/Classical_Supervised_Learning/Model_Selection,
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chapters/Classical_Supervised_Learning/k-Nearest_Neighbors,
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chapters/Classical_Supervised_Learning/Trees_and_Forests,
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%Kernel_Methods
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chapters/Kernel_Methods/Kernel-Regression,
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chapters/Kernel_Methods/Support_Vector_Machines,
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chapters/Bayesian_Learning/Bayesian_Learning,
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chapters/Bayesian_Learning/Bayesian_Regression_Algorithms,
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%Neural_Networks
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chapters/Neural_Networks/Basics,
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chapters/Neural_Networks/Gradient_Descent,
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chapters/Neural_Networks/Regularization,
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chapters/Neural_Networks/Practical_Considerations,
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chapters/Neural_Networks/CNNs_and_RNNs,
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%Classical_Unsupervised_Learning
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chapters/Classical_Unsupervised_Learning/Dimensionality_Reduction_and_Clustering,
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chapters/Classical_Unsupervised_Learning/Density_Estimation_and_Mixture_Models,
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chapters/Classical_Unsupervised_Learning/Variational_Auto-Encoders,
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%Mathematische_Grundlagen
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chapters/Mathematische_Grundlagen/Lineare_Algebra,
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chapters/Mathematische_Grundlagen/Probability_Theory,
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chapters/Mathematische_Grundlagen/Kernel_Basics,
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chapters/Mathematische_Grundlagen/Sub-Gradients,
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chapters/Mathematische_Grundlagen/Constraint_Optimization,
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chapters/Mathematische_Grundlagen/Gaussian_Identities,
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%Anhang
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Appendix
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}
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\input{Glossary.tex}
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\begin{document}
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% {{{ Main Content%
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\pagenumbering{arabic}
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\part{Einleitung}
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\input{chapters/Einleitung.tex}
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\include{chapters/Einleitung.tex}
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\part{Classical Supervised Learning}
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\label{part:Classical Supervised Learning}
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\input{chapters/Classical_Supervised_Learning/Linear_Regression.tex}
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\input{chapters/Classical_Supervised_Learning/Linear_Classification.tex}
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\input{chapters/Classical_Supervised_Learning/Model_Selection.tex}
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\input{chapters/Classical_Supervised_Learning/k-Nearest_Neighbors.tex}
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\input{chapters/Classical_Supervised_Learning/Trees_and_Forests.tex}
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\include{chapters/Classical_Supervised_Learning/Linear_Regression.tex}
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\include{chapters/Classical_Supervised_Learning/Linear_Classification.tex}
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\include{chapters/Classical_Supervised_Learning/Model_Selection.tex}
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\include{chapters/Classical_Supervised_Learning/k-Nearest_Neighbors.tex}
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\include{chapters/Classical_Supervised_Learning/Trees_and_Forests.tex}
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\part{Kernel Methods}
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\label{part:Kernel Methods}
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\input{chapters/Kernel_Methods/Kernel-Regression.tex}
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\input{chapters/Kernel_Methods/Support_Vector_Machines.tex}
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\include{chapters/Kernel_Methods/Kernel-Regression.tex}
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\include{chapters/Kernel_Methods/Support_Vector_Machines.tex}
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\part{Bayesian Learning}
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\label{part:Bayesian Learning}
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\input{chapters/Bayesian_Learning/Bayesian_Learning.tex}
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\input{chapters/Bayesian_Learning/Bayesian_Regression_Algorithms.tex}
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\include{chapters/Bayesian_Learning/Bayesian_Learning.tex}
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\include{chapters/Bayesian_Learning/Bayesian_Regression_Algorithms.tex}
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\part{Neural Networks}
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\label{part:Neural Networks}
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\input{chapters/Neural_Networks/Basics.tex}
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\input{chapters/Neural_Networks/Gradient_Descent.tex}
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\input{chapters/Neural_Networks/Regularization.tex}
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\input{chapters/Neural_Networks/Practical_Considerations.tex}
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\input{chapters/Neural_Networks/CNNs_and_LSTMs.tex}
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\include{chapters/Neural_Networks/Basics.tex}
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\include{chapters/Neural_Networks/Gradient_Descent.tex}
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\include{chapters/Neural_Networks/Regularization.tex}
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\include{chapters/Neural_Networks/Practical_Considerations.tex}
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\include{chapters/Neural_Networks/CNNs_and_RNNs.tex}
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\part{Classical Unsupervised Learning}
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\label{part:Classical Unsupervised Learning}
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\input{chapters/Classical_Unsupervised_Learning/Dimensionality_Reduction_and_Clustering.tex}
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\input{chapters/Classical_Unsupervised_Learning/Density_Estimation_and_Mixture_Models.tex}
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\input{chapters/Classical_Unsupervised_Learning/Variational_Auto-Encoders.tex}
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\include{chapters/Classical_Unsupervised_Learning/Dimensionality_Reduction_and_Clustering.tex}
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\include{chapters/Classical_Unsupervised_Learning/Density_Estimation_and_Mixture_Models.tex}
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\include{chapters/Classical_Unsupervised_Learning/Variational_Auto-Encoders.tex}
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\part{Mathematische Grundlagen}
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\label{part:Mathematische Grundlagen}
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\input{chapters/Mathematische_Grundlagen/Lineare_Algebra.tex}
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\input{chapters/Mathematische_Grundlagen/Probability_Theory.tex}
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\input{chapters/Mathematische_Grundlagen/Kernel_Basics.tex}
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\input{chapters/Mathematische_Grundlagen/Sub-Gradients.tex}
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\input{chapters/Mathematische_Grundlagen/Constraint_Optimization.tex}
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\input{chapters/Mathematische_Grundlagen/Gaussian_Identities.tex}
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\include{chapters/Mathematische_Grundlagen/Lineare_Algebra.tex}
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\include{chapters/Mathematische_Grundlagen/Probability_Theory.tex}
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\include{chapters/Mathematische_Grundlagen/Kernel_Basics.tex}
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\include{chapters/Mathematische_Grundlagen/Sub-Gradients.tex}
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\include{chapters/Mathematische_Grundlagen/Constraint_Optimization.tex}
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\include{chapters/Mathematische_Grundlagen/Gaussian_Identities.tex}
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% }}} %
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\input{Appendix.tex}
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\pagenumbering{roman}
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%glossary
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\printglossary[nonumberlist]
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\newpage
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%bibliography
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\phantomsection
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\addcontentsline{toc}{chapter}{Literatur}
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\bibliographystyle{IEEEtran-de}
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\bibliography{Bibliography.bib}
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\include{Appendix.tex}
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\end{document}
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\chapter{CNNs and LSTMs}%
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\label{cha:CNNs and LSTMs}
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2
chapters/Neural_Networks/CNNs_and_RNNs.tex
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2
chapters/Neural_Networks/CNNs_and_RNNs.tex
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\chapter{\texorpdfstring{\glsxtrshortpl{CNN} and \glsxtrshortpl{RNN}}{\glsfmtshortpl{CNN} and \glsfmtshortpl{RNN}}}%
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\label{cha:CNNs and RNNs}
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