This python notebook follows neural networks tutorial series by Welch Labs.Link :- http://www.welchlabs.com/blog/2015/1/16/neural-networks-demystified-part-1-data-and-architecture#
Part 1: Data + Architecture
http://nbviewer.jupyter.org/github/stephencwelch/Neural-Networks-Demysitifed/blob/master/Part%201%20Data%20and%20Architecture.ipynb
Part 2: Forward Propagation
http://nbviewer.jupyter.org/github/stephencwelch/Neural-Networks-Demysitifed/blob/master/Part%202%20Forward%20Propagation.ipynb
Part 3: Gradient Descent
http://nbviewer.jupyter.org/github/stephencwelch/Neural-Networks-Demysitifed/blob/master/Part%203%20Gradient%20Descent.ipynb
Part 4: Backpropagation
http://nbviewer.jupyter.org/github/stephencwelch/Neural-Networks-Demysitifed/blob/master/Part%204%20Backpropagation.ipynb
Part 5: Numerical Gradient Checking
http://nbviewer.jupyter.org/github/stephencwelch/Neural-Networks-Demysitifed/blob/master/Part%205%20Numerical%20Gradient%20Checking.ipynb
Part 6: Training
http://nbviewer.jupyter.org/github/stephencwelch/Neural-Networks-Demysitifed/blob/master/Part%206%20Training.ipynb
Part 7: Overfitting, Testing, and Regularization