Having completed, as one of the first, the Udacity Self Driving Car Nano Degree in October 2017, I thought I’d share some of the things I learnt along the way.
Rather than recap the projects, over the three terms, I’m going to focus in this post, on the philosophy and attitude I developed, to complete the nano degree program.
When I was first accepted, my initial reaction was geez have I bitten off more than I can chew. How am I going to cope with the mathematics and the theoretical side. It was a major concern.
Whilst at school I always had excelled at maths, and it was what led me initially into computing at a young age. I used to love writing graphics routines and optimising them. As my maths skills improved I learnt new ways of drawing circles and objects. I can’t remember if I got into Vectors. Yet past school, having started working in corporate IT, I had little use for maths skills besides that which was needed for Accounting. Yes for a number of years, IT dumbed down my maths skills.
IT was more focused on entering data, storing it and reporting on it at some monthly and yearly aggregate levels. Sure I worked on near realtime and mission critical systems but the need for very strong maths skills was limited. It was not a choice of my own, it was just that the technology that did leverage Maths, was perceived as scientific or too risky to adopt by business. It just didn’t have priority or urgency. Or if it was implemented it was a black box, that you supplied some input to, and you just consumed the output.
Getting back to the Self Driving Car Nano Degree, it was these black boxes that were our projects. In the project we needed to create the black boxes, to understand the theory and the mathematics.
Before starting the Nano Degree, I brushed up on matrices and vectors using the Kahn Academy.
Occasionally I got a little stuck on the mathematical proofs but once I understood the code for the maths, I normally was ok. Yes my brain now works off of code, not maths. We experienced some numerical instability, which was normally solved by interacting with others on the slack channels.
It was hard at times being the first going through the material. However with patience, with continuously reviewing the material, it reinforced what was being taught. You had to be methodical and test each assertion you were making about your code. Sometimes it required taking the algorithm and implementing in a repeatable test case inside a Jupyter Notebook. I found visualising the data improved understanding and helped to identify if anything was erroneous.
You could spend ages looking at the code and not see any obvious mistake. Without visualising the output, an easy mistake such as an incorrect sign in a rotation matrix, was not easy to observe.
The most valuable tool for when you got stuck was slack and your fellow students. These fellow students were online at all hours of the day, from across the globe.
After a few projects, I soon found an approach, that worked for me. It boiled down to learning, writing some code, seeing what happened, fixing what was broken, validating my learning and repeating until I had a project that met requirements.
Getting stuck, sometimes meant having a break, or having a late night. If I was really into tuning, it often meant the late night. Tweaking and trying different settings to get the Neural Network or Algorithm to achieve what you needed, was addictive. It was so much better, then reading or watching a video. The impact of changing your code was visible, in most projects in the simulator.
Your code didn’t produce a report, it produced observable action! It was like when I was a kid programming graphics for the first time.
So if your the type that likes to write those black boxes that other programmers use, you’ll excel at this Nano Degree. If your the type that consumes black boxes, that others have written, you may need to change your outlook.
Nick Hortovanyi's blog 