Here are some great how-to guides from Udacity students! Everything from how to find a job to how to build a self-driving (minature) car 🙂
George landed a job working on deep learning with BMW’s autonomous vehicle team in Silicon Valley! His stats on the hiring funnel are instructive for anybody interviewing in software, and especially in this industry.
“I had 9 interviews out of my ~90 job applications, i.e. around 10% of applications lead to interviews. In my mind this was a pretty good conversion rate. Out of those 9 interviews, 4 of them lead to final-round interviews: 2 final-round interviews for full-time jobs, 2 final-round interviews for internships. I did well on those 4 interviews as they all lead to offers.”
Galen got a job working on autonomous vehicles at HERE’s Boulder, Colorado, office! It’s also a great example of how being flexible about roles (Galen is starting on the DevOps team) can help you get a foot in the door with autonomous vehicle teams.
“Mathematics is a wonderful thing, but it’s not very career specific. Just a few months after graduating, I made two very important decisions: to enroll at Metis and to enroll in the Udacity Self-driving Car Engineer Nanodegree (SDCEND). Both of these were instrumental in my career path, but the Udacity SDCEND was critical.”
Ubuntu + Deep Learning Software Installation Guide
In the Udacity Self-Driving Car Nanodegree Program, we provide an AWS AMI for utilizing NVIDIA GPUs for accelerating deep learning. We don’t, however, explain how to set up this software on your own machine. Probably we should do that. In the meantime, Nick has this terrific guide.
“There are a number of good installation guides out there — particularly this one from floydhub that much of this is based on — but I found myself having to dig through many different resources to get everything installed properly. The goal of this article is to consolidate all the necessary resources into one place.”
“ros skillz pay Jari’s billz”, and here he walks through how to get ROS set up using the Docker virtual environment.
“Image: This is essentially the “installation” of something that you want to run using Docker. An image contains all the data necessary to run containers. Images are hierarchical and a new image that shares information with an older one will not reproduce this information and instead just re-use it (i.e. if you have two Ubuntu based images with different software installed, they will both refer to the same base Ubuntu image rather than copy its contents). This is what people mean when they say that Docker’s filesystem is layered.”
This is the first part of Yazeed’s multi-part series on how to build a deep-learning powered miniature autonomous vehicle. Super cool!
“I decided to build my first self-driving car, I mean RC Car 😅 . I think I already have the knowledge and tools to start crafting my RC’s future.”