http://xgboost.readthedocs.io/en/latest/build.html

https://archive.ics.uci.edu/ml/datasets.html

https://keras.io/preprocessing/image/#imagedatagenerator

https://keras.io/

http://peterroelants.github.io/posts/neural_network_implementation_intermezzo02/

http://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/

https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html

http://cs231n.github.io/

http://scs.ryerson.ca/~aharley/vis/conv/flat.html

http://ais.uni-bonn.de/papers/icann2010_maxpool.pdf

https://docs.gimp.org/en/plug-in-convmatrix.html

https://cs.nju.edu.cn/wujx/paper/CNN.pdf

http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf

http://neuralnetworksanddeeplearning.com/chap2.html

http://neuralnetworksanddeeplearning.com/

http://iamtrask.github.io/2015/07/27/python-network-part2/

http://iamtrask.github.io/

https://stats.stackexchange.com/questions/154879/a-list-of-cost-functions-used-in-neural-networks-alongside-applications

http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf

http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf

http://yann.lecun.com/exdb/

http://mlkernels.readthedocs.io/en/latest/kernels.html

https://cran.r-project.org/

https://www.rstudio.com/

https://www.continuum.io/downloads

https://www.superdatascience.com/machine-learning/

https://www.superdatascience.com/sds-002-machine-learning-recommender-systems-and-the-future-of-data-with-hadelin-de-ponteves/