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Finish project. Hopefully

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report/chapters/introduction.tex
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Optimizing numerical models to model a set of data has been a challenge of the natural sciences for many decades. But with the introduction of artificial neural networks in all disciplines over the last years a special focus on those optimization techniques is relevant.
In this paper different methods of such optimization are studied in the context of large datasets and big data with possibly noise-affected input data. To render the best possible fits a multitude of cost functions, minimization algorithms and other approaches to reduce the computation associated with the optimization process are studied and evaluated.
In this paper different methods of such optimization are studied in the context of large datasets and big data with possibly noise-affected input data. To render the best possible fits a multitude of cost functions, minimization algorithms and other approaches to reduce the computation associated with the optimization process are studied and evaluated. Reducing the computational cost is also important due improve the economical and ecological footprint of training large models on big data, as the spread of datadriven methods in all areas of life is increasing rapidly.
In the following section the theoretical background of the methods used in this paper is presented. In \cref{sec:methods} secondly the implementation of these methods is discussed. The results of applying these methods to different datasets are presented in \cref{sec:results}. Finally, a conclusion is drawn in \cref{sec:conclusion} and an outlook on possible future work is given in \cref{sec:perspective}.