PRE2019 3 Group10
Group Members
| Name | Student Number | Study | |
|---|---|---|---|
| Joris Goddijn | 1244648 | Computer Science | j.d.goddijn@student.tue.nl |
| Daniël Verloop | 1263544 | Computer Science | a.c.verloop@student.tue.nl |
| Ka Yip Fung | 1245300 | Computer Science | k.y.fung@student.tue.nl |
| Rik Maas | 1244503 | Computer Science | r.maas@student.tue.nl |
| Jeroen Struijk | 1252070 | Computer Science | j.j.struijk@student.tue.nl |
Problem Statement
Neural Networks have been used extensively in a wide variety of fields. In health care, neural networks are used for detecting certain diseases, for example tumor detection. In businesses, neural networks are used to filter job applicants: this way the business only has to take the best applicants in for interviews. In self driving cars neural networks are used to parse the visual input of the sensors of the car.
A big issue of modern neural networks is that it is a black box approach. Experts do not understand what is going on inside the neural network. This is an issue when people use neural networks to make decisions with real impactful consequences.
Our aim is to provide a tool which our users can use to have a better understanding of how the neural network they are using is making decisions. We are going to make a web application in which users can see visualizations of the inner workings of a neural network. We will focus on showing visualizations of the individual layers of a neural network. The goal of our web application is to help users understand how their neural network is making decisions.