@online{hand11neur,
    author  = {Roman V Belavkin},
    title   = {Lecture 11: Feed-Forward Neural Networks},
    note    = {A Model of A Single Neuron},
    urldate = {2010-03-17},
    date    = {2016-03-12},
    pages   = {3 - 5},
    url = {http://eis.mdx.ac.uk/staffpages/rvb/teaching/BIS3226/hand11.pdf}
}
@online{hand11alg,
    author  = {Roman V Belavkin},
    title   = {Lecture 11: Feed-Forward Neural Networks},
    note    = {Training algorithms},
    urldate = {2010-03-17},
    date    = {2016-03-12},
    pages   = {9},
    url = {http://eis.mdx.ac.uk/staffpages/rvb/teaching/BIS3226/hand11.pdf}
}
@online{hand11c,
    author  = {Roman V Belavkin},
    title   = {Lecture 11: Feed-Forward Neural Networks},
    note    = {Applications},
    urldate = {2010-03-17},
    date    = {2016-03-12},
    pages   = {10f},
    url = {http://eis.mdx.ac.uk/staffpages/rvb/teaching/BIS3226/hand11.pdf}
}
@online{welt,
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    urldate = {2014-02-09},
    date   = {2016-03-13},
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}
@online{cancer,
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    title   = {Neuron},
    urldate = {2006-12-19},
    date    = {2016-03-12},
    url = {https://commons.wikimedia.org/wiki/File:Neuron_\%28deutsch\%29-1.svg}
}
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    note = {Rezeptor},
    urldate = {2015-01-19},
    date = {2016-03-12},
    url = {http://flexikon.doccheck.com/de/Rezeptor}
}
@online{spektrum,
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    author = {David Kriesel},
    title = {Ein kleiner Überblick über Neuronale Netze},
    note = {Zur Geschichte Neuronaler Netze},
    pages = {10ff.},
    urldate = {2007},
    date = {2016-03-12},
    url = {http://www.dkriesel.com/_media/science/neuronalenetze-de-zeta2-1col-dkrieselcom.pdf}
}
@online{chrislb,
    author = {Wikimedia Commons Chrislb},
    title = {Schema eines künstlichen Neurons},
    date = {2016-03-12},
    urldate = {2005-07-14},
    url = {https://commons.wikimedia.org/wiki/File:ArtificialNeuronModel_deutsch.png}
}
@online{gershenson,
    author = {Carlos Gershenson},
    title = {Artificial Neural Networks for beginners},
    pages = {3ff.},
    urldate = {2003-08-20},
    date = {2016-03-12},
    url = {http://arxiv.org/pdf/cs/0308031v1}
}
@online{praat,
    author = {Paul Boersma},
    title = {Feedforward neural networks 1},
    note = {What is a feedforward neural network},
    urldate = {2004-04-26},
    date = {2016-03-12},
    url = {http://www.fon.hum.uva.nl/praat/manual/Feedforward_neural_networks_1__What_is_a_feedforward_ne.html}
}
@online{sky99,
    author = {Wikimedia Commons Sky99},
    title = {Struktur eines klassischen Multi-Layer-Perzeptrons},
    urldate = {2012-04-15},
    date = {2016-03-12},
    url = {https://commons.wikimedia.org/wiki/File:MultiLayerPerceptron.svg}
}
@online{dongsoo,
    author = {Anthony J. Papagelis and Dong Soo Kim},
    title = {Multi-Layer Perceptron},
    note = {Backpropagation},
    urldate = {2004-08-28},
    date = {2016-03-12},
    url = {https://www.cse.unsw.edu.au/~cs9417ml/MLP2/BackPropagation.html#BackPropagation}
}
@online{facklam,
    author = {Yannik Facklam},
    title = {Entwicklung und Analyse eines Intraday-Marktpreismodells für liquide US-Aktien mit Künstlichen Neuronalen Netzen},
    pages = {79},
    urldate = {2013-09-27},
    date = {2016-03-12},
    url = {http://www.iwi.uni-hannover.de/fileadmin/wirtschaftsinformatik/Abschlussarbeiten/MA_Facklam_Y._K..pdf}
}
@online{harrison,
    author = {M. Lichman},
    title = {UCI Machine Learning Repository},
    note = {The Boston Housing Dataset, übersetzt},
    urldate = {1996-10-10},
    date = {2016-03-12},
    url = {https://archive.ics.uci.edu/ml/datasets/Housing}
}
@online{gbrainteam,
    author = {Tensorflow, Google Brain Team},
    title = {Open Source Library Tensorflow},
    note = {Python API documentation},
    urldate = {2016-02-16},
    date = {2016-03-12},
    url = {https://www.tensorflow.org/versions/master/api_docs/python/train.html#exponential_decay}
}
@online{sbernd,
    author = {Bernd Schneider},
    title = {Neuronale Netze für betriebliche Anwendungen. Anwendungspotentiale und existierende Systeme},
    note = {Datenanalysen mit neuronalen Netzen},
    pages = {14ff.},
    urldate = {1993-11},
    date = {2016-03-12},
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}
@online{neuraltalk,
    author = {Andrej Karpathy and Justin Johnson},
    title = {neuraltalk2},
    urldate = {2015-11-15},
    date = {2016-03-12},
    url = {https://github.com/karpathy/neuraltalk2}
}
@online{charrnn,
    author = {Andrej Karpathy},
    title = {Andrej Karpathy blog},
    note = {The Unreasonable Effectiveness of Recurrent Neural Networks},
    urldate = {2015-03-21},
    date = {2016-03-12},
    url = {https://karpathy.github.io/2015/05/21/rnn-effectiveness/}
}
@online{storyteller,
    author = {samim},
    title = {Generating Stories about Images. Recurrent neural network for generating stories about images},
    urldate = {2015-11-05},
    date = {2016-03-12},
    url = {https://medium.com/@samim/generating-stories-about-images-d163ba41e4ed#.618pkay3f}
}
@online{twominutepapers,
    author = {Károly Zsolnai-Fehér},
    title = {Two Minute Papers. Deep Neural Network Learns Van Gogh's Art},
    urldate = {2015-08-29},
    date = {2016-03-12},
    url = {https://www.youtube.com/watch?v=-R9bJGNHltQ}
}
@online{mario,
    author = {SethBling},
    title = {MarI/O. Machine Learning for Video Games},
    urldate = {2015-06-13},
    date = {2016-03-12},
    url = {https://www.youtube.com/watch?v=qv6UVOQ0F44}
}
@online{alphago,
    author = {David Silver and Demis Hassabis},
    title = {Google Research Blog},
    note = {AlphaGo. Mastering the ancient game of Go with Machine Learning},
    urldate = {2016-01-27},
    date = {2016-03-12},
    url = {http://googleresearch.blogspot.de/2016/01/alphago-mastering-ancient-game-of-go.html}
}