Easy to learn
Very readable code (easy to follow structure)
Procedural, object-oriented and functional element are all present to some extent
Available on all platforms
Completele free
Widely used in scientific computing (as powerful as MATLAB, Mathematica or Maple)
Widely used for general programming tasks
1980s Guido van Rossum starts working on Python
1989 first implementation ready
1994 Python 1.0
2008 Python 2to3 transition started
Current versions 3.5.0 and 2.7.10
2 to 3 transition almost finished https://wiki.python.org/moin/Python2orPython3
https://docs.python.org/2/tutorial/index.html
http://ipython.org/notebook.html
http://jupyter.readthedocs.org/en/latest/index.html
http://nbviewer.ipython.org/github/masinoa/machine_learning/blob/master/04_Neural_Networks.ipynb
https://repl.it/languages/python3
Neurolab code
https://github.com/blagasz/neurolab
Neurolab documentation
https://pythonhosted.org/neurolab/index.html
NEURAL NETWORKS by Christos Stergiou and Dimitrios Siganos
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
Neural Network Back-Propagation using Python
https://visualstudiomagazine.com/articles/2014/12/01/back-propagation-using-python.aspx
Neural Networks with backpropagation for XOR using one hidden layer
Simple Back-propagation Neural Network in Python source code
http://code.activestate.com/recipes/578148-simple-back-propagation-neural-network-in-python-s/
To find the most convenient syntax for creating neural networks and using them is not straight forward.
Standardize input data
Usually need to experiment with network layout
Let the number of neurons in each layer be m for input layer neurons, n for
output layer neurons and p for the hidden layer, then the training data
input-output numbers should be minimum 10 times (m+1)p + (p+1)n.
The input-output traning pair should be divided in 50:25:25 into training, testing, and actual working data.