sources.txt

https://de.wikipedia.org/wiki/Neuronales_Netz wikipedia for general info
http://www.mathematik.uni-ulm.de/stochastik/lehre/ss07/seminar_sl/wallner.pdf
https://www.brgdomath.com/psychologie/lernen-und-ged%C3%A4chtnis-tk-3/was-passiert-beim-lernen-im-gehirn/
https://de.wikipedia.org/wiki/Neuronale_Plastizit%C3%A4t
https://pdfs.semanticscholar.org/09cf/727949c915ae8fd8b0a2ead55bcafedc0a12.pdf
https://ccrma.stanford.edu/~eberdahl/Projects/Vokalerkennung/NN_Doku.pdf
http://eis.mdx.ac.uk/staffpages/rvb/teaching/BIS3226/hand11.pdf nice paper cutting the pdf name away and there you can find more papers
https://stats.stackexchange.com/questions/2213/whats-the-difference-between-feed-forward-and-recurrent-neural-networks
https://github.com/llSourcell/Convolutional_neural_network/blob/master/convolutional_network_tutorial.ipynb how a CNN works
https://www.youtube.com/watch?v=FTr3n7uBIuE CNNs coding exaple
https://en.wikipedia.org/wiki/Recurrent_neural_network # recurrent NN
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ recurrent NN
https://www.youtube.com/watch?v=9zhrxE5PQgY LSTM network explained
https://www.quora.com/How-does-a-deconvolutional-neural-network-work see link name
https://www.slideshare.net/nmhkahn/deconvnet-decouplednet-transfernet-in-image-segmentation Deconvolutional Network slide show
https://quizlet.com/152146730/neural-network-zoo-flash-cards/
https://towardsdatascience.com/the-8-neural-network-architectures-machine-learning-researchers-need-to-learn-11a0c96d6073
https://medium.com/technology-nineleaps/vectors-in-machine-learning-b8dbdae53aa0
https://www.youtube.com/watch?v=HHUqhVzctQE understanding vectors
https://www.youtube.com/watch?v=-7scQpJT7uo  wich activation function to use
http://www.neuronalesnetz.de/verbindungen.html
https://www.quora.com/What-is-the-learning-rate-in-neural-networks nice example for learning rate
https://stackoverflow.com/questions/34518656/how-to-interpret-loss-and-accuracy-for-a-machine-learning-model#34519264
https://en.wikipedia.org/wiki/Overfitting what is overfitting
https://www.youtube.com/watch?v=OVHc-7GYRo4 The perceptron
https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks the bias
https://www.quora.com/What-is-bias-in-artificial-neural-network?share=1 and againe
https://stats.stackexchange.com/questions/185911/why-are-bias-nodes-used-in-neural-networks and againe
https://stackoverflow.com/questions/3985619/how-to-calculate-a-logistic-sigmoid-function-in-python sigmoid function
https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1 simple ANN in python
https://page.mi.fu-berlin.de/rojas/neural/chapter/K4.pdf research paper
https://medium.com/technology-invention-and-more/everything-you-need-to-know-about-artificial-neural-networks-57fac18245a1 good starting point for Neuronal networks
https://www.youtube.com/watch?v=ILsA4nyG7I0 # basic intro to deep neuronal networks and how they work with an example
https://www.youtube.com/watch?v=IHZwWFHWa-w second part; backpropagation
https://www.youtube.com/watch?v=Ilg3gGewQ5U third part
https://www.youtube.com/watch?v=m0pIlLfpXWE nice video about activation functions
https://www.youtube.com/watch?v=q555kfIFUCM backpropagation in 5 min.
https://stats.stackexchange.com/questions/179026/objective-function-cost-function-loss-function-are-they-the-same-thing what is a loss function, a objectiv function and a cost function
https://machinelearningmastery.com/generate-test-datasets-python-scikit-learn/ how to generate sample data with scikitlearn
https://beckernick.github.io/neural-network-scratch/ used to get a general idea of a real network
https://dzone.com/articles/the-very-basic-introduction-to-feed-forward-neural here as well
https://en.wikipedia.org/wiki/Mean_squared_error mean_squarred_error
http://python3.codes/neural-network-python-part-1-sigmoid-function-gradient-descent-backpropagation/ easy stuff to gradient descent, not as deep as needed but usefull for coding
https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw how layers are setted up
https://www.statworx.com/de/blog/wie-lernen-neuronale-netze/ basic information in german have to check it out againe
https://analyticsindiamag.com/6-types-of-artificial-neural-networks-currently-being-used-in-todays-technology/ types of neural Networks and their usage; need checkout
https://www.tensorflow.org/versions/master/tutorials/layers usefull for tensorflow and in general
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ very good guide for backpropagation with example
https://www.youtube.com/watch?v=tIeHLnjs5U8  highly recommended!


special tools:
https://www.matheretter.de/tools/formeleditor/ here you can create math formulas
https://www.khanacademy.org/test-prep/fr-twelveth-grade-math/les-derivees/introduction-aux-derivees/v/calculus-derivatives-1 for deriviative