This work was a part of a machine learning course I took during my Semester at the VUB in 2017. The course is given by Ann Nowé and Hugues Bersini. This notebook is a slightly adapted form of the work I did for the final project of this course. It takes former created ground truth data information and uses it as an input to train and test a multi layer perceptron (Courtesy to Frederik PRIEM who created the ground truth data). The code is heavily inspired by the tutorial "implementing a neural network from scratch" by Dennie Britz. It is strongly recommended to also read his post to understand of what is going on.
The code and the data can be found here on my github page.