The goal of forking from https://github.com/MichalDanielDobrzanski/DeepLearningPython35 was to do the last exercise in chapter 2 of "Neural Networks and Deep Learning by Michael Nielsen" where the author asks to "Modify network.py so that it uses this fully matrix-based approach". The result is in network_mat.py. I changed only the update_mini_batch function of original network.py.
The test_mat.py is for comparing the performances of network.py and network_mat.py.
Modified network2.py to be able to use L1 regularization (see problem here) and momentum-based stochastic gradient descent (see problem here).
These scrips are updated ones from the neuralnetworksanddeeplearning.com gitHub repository in order to work with Python 3.5.2
The testing file (test.py) contains all three networks (network.py, network2.py, network3.py) from the book and it is the starting point to run (i.e. train and evaluate) them.
In test.py there are examples of networks configurations with proper comments. I did that to relate with particular chapters from the book.