CoCalc is a web-based cloud computing and course management platform for computational mathematics based on Jupyter. I was lucky to have them as Juno launch partner, since for a truly seamless Jupyter experience Juno needed a cloud computing service integration that would let you start coding right out of the box.
I have been a huge fan of Jupyter for a while now, and most importantly of the flexibility it is offering: I strongly believe that the fact that you only need a screen and network connection to get access to pretty much unlimited computational resources has enormous potential.
In order to use Jupyter Notebook on iPad, one needs to correctly configure SSL certificates. Since issuing a proper certificate from a trusted authority could be challenging in some cases, a self-signed certificate should suffice, provided it was signed by a CA that is trusted by device. Follow these steps to get it working on your iPad!
Arguably the most essential piece of hardware for a self-driving car setup is a lidar. A lidar allows to collect precise distances to nearby objects by continuously scanning vehicle surroundings with a beam of laser light, and measuring how long it took the reflected pulses to travel back to sensor.
The goal of this project was to try and detect a set of road features in a forward facing vehicle camera data. This is a somewhat naive way as it is mainly using computer vision techniques (no relation to naive Bayesian!). Features we are going to detect and track are lane boundaries and surrounding vehicles.