Hi, I’m Tom

In this post, I want to briefly introduce Differential Privacy to you, which, in my honest opinion, needs to get more attention in the software developer community. During my Master thesis, I evaluated the use of Differential Privacy for Federated Learning (I might explain Federated Learning in another post). The Theory Differential Privacy, originally $\epsilon$-Differential Privacy (DP)1, is a way to secure the privacy of individuals in a statistical database. A statistical database is a database, where only aggregation functions like “sum”, “average”, “count”, et cetera… can be executed. Read more

Did you ever wonder what it would look like if we combine a chair and an airplane? Turns out it doesn’t look that good. Before we get to this Chairplane, lets first outline this post’s topic: I am going to demonstrate you some possibilities of an 3D Generative Adversarial Network. That is: A GAN not applied to images, but to 3D Voxel objects. But what is a GAN? A GAN consists of two deep neural networks that try to game each other. Read more