References

[1]

Christopher Bishop. Pattern Recognition and Machine Learning. Springer, New York, 1st ed. 2006. corr. 2nd printing 2011 edition, 2007. ISBN 978-0-387-31073-2. URL: https://www.amazon.de/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8\&qid=1475680474\&sr=8-1\&keywords=Pattern+Recognition+and+Machine+Learning.

[2]	Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, Cambridge, UK ; New York, 2004. ISBN 978-0-521-83378-3.

[3]	Balázs Csanád Csáji. Approximation with Artificial Neural Networks. MSc Thesis, Eötvös Loránd University (ELTE), Budapest, Hungary, 2001. URL: https://pdfs.semanticscholar.org/3527/2edb07ee4f0c9957eb0c631d2a74c24fe446.pdf.

[4]	Matplotlib development team. Matplotlib documentation. 2016. URL: http://matplotlib.org/contents.html.

[5]	Numpy development team. Numpy documentation. 2016. URL: http://docs.scipy.org/doc/numpy/.

[6]	Python development team. Python 3 Documentation. 2016. URL: https://docs.python.org/3/.

[7]	Scipy development team. Scipy Lecture Notes. 2016. URL: http://www.scipy-lectures.org/index.html.

[8]	Florian Jarre. Optimierung. Springer, Berlin u.a., 2004 edition, 2013. ISBN 978-3-540-43575-4.

[9]	Warren S. McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4):115–133, 1943. URL: http://link.springer.com/article/10.1007/BF02478259, doi:10.1007/BF02478259.

[10]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, February 2015. URL: http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html, doi:10.1038/nature14236.

[11]	Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. Mit University Press Group Ltd, Cambridge, MA, 2012. ISBN 978-0-262-01825-8. URL: https://www.amazon.de/Foundations-Machine-Learning-Adaptive-Computation/dp/026201825X/ref=asap_bc?ie=UTF8.

[12]	Michael A. Nielsen. Neural Networks and Deep Learning. 2015. URL: http://neuralnetworksanddeeplearning.com.

[13]	Sebastian Raschka. Python Machine Learning. Packt Publishing - ebooks Account, September 2015. ISBN 978-1-78355-513-0. URL: https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1478543661&sr=8-1.

[14]	search results and search results. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York, NY, USA, 1 edition edition, May 2014. ISBN 978-1-107-05713-5. URL: https://www.amazon.com/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132.

[15]	F. Rosenblatt. The perceptron, a perceiving and recognizing automaton. Project Para. Cornell Aeronautical Laborator, 1957.

[16]	F. Rosenblatt. The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain. Psychological Review, pages 65–386, 1958.

[17]	David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533–536, October 1986. URL: http://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html, doi:10.1038/323533a0.

[18]

Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River, 00003 edition, 2010. ISBN 978-0-13-604259-4. URL: https://www.amazon.de/Artificial-Intelligence-Modern-Approach-Global-ebook/dp/B01HEY2P66/ref=la_B001IGQQBC_1_1?s=books&ie=UTF8&qid=1523302737&sr=1-1.

[19]	A. M. Turing. I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind, LIX(236):433–460, 1950. URL: http://mind.oxfordjournals.org/cgi/doi/10.1093/mind/LIX.236.433, doi:10.1093/mind/LIX.236.433.

[20]	Vladimir N. Vapnik. Statistical Learning Theory. Wiley, New York, 1 edition edition, 1998. ISBN 978-81-265-2892-9.

[21]	Peter Varga and Raúl Rojas. Neural Networks: A Systematic Introduction. Springer Berlin Heidelberg, Berlin ; New York, January 1996. ISBN 978-3-540-60505-8. URL: https://page.mi.fu-berlin.de/rojas/neural/.

[22]	B. Widrow and M.E. Hoff. Adapting switching circuits. IRE WESCON Convention record, pages 96–104, 1960. URL: http://www-isl.stanford.edu/people/widrow/papers/c1960adaptiveswitching.pdf.