Udacity Students on Deep Learning, Hiring, and CarND
Five Udacity students muse about deep learning, hiring, and the Self-Driving Car Program.
Behavioral Cloning — Self-Driving Car Simulation
Jonathan’s post has a really nice walkthrough of his behavioral cloning project, including a visual explanation of the data pre-processing pipeline:
“Our data comes in as 160 x 320 x 3 RGB images. I used several preprocessing techniques to augment, transform, and create more data to give my network a better chance at generalizing to different track features.”
Vehicle tracking using a support vector machine vs. YOLO
Kaspar has a terrific comparison of vehicle detection pipelines using standard computer vision, compared with a deep learning solution using YOLO:
“A forward pass of an entire image through the network is more expensive than extracting a feature vector of an image patch and passing it through an SVM. Hoever, this operation needs to be done exactly once for an entire image, as opposed to the roughly 150 times in the SVM+HOG approach. For generating the video above I did no performance optimization, like reducing the image or defining a region of interest, or even training specifically for cars. Nevertheless, YOLO is more than 20x faster than the SVM+HOG and at least as accurate.”
Five Skills Self-Driving Companies Need
Caleb put together an awesome list of autonomous vehicle skills, and which companies are looking for them:
“The one constant in all of the postings is that experience with programming in C++ is a highly sought attribute for self-driving companies. Since performance is so vital for any code running on a real time system, it’s necessary to use a language that can be compiled to machine code for speed.”
Machine versus human learning in traffic sign classification
Arnaldo has a fun comparison of how machine learning compares to human learning, specifically applied to the Traffic Sign Classifier Project:
“Overfit: the guy who has a perfect grade in school, in all subjects, but outside school knows nothing in real world. Or someone who has a phD in nuclear advanced theoretical gravitational quantum physics, but works as a waiter in a restaurant, because his knowledge is so specific it has no real world application.”
Self-Driving Car Engineer Diary — 7
Andrew has a generous assessment of Term 1 of CarND:
““Amazing projects. Steep learning curve. Strong student community. Incredibly supportive and adaptive Udacity staff. Be prepared to commit 2–3 times estimated 10 hours per week to complete Term 1 successfully as projects encourage experimentation. Now to catch-up on sleep before the start of Term 2 on 24/Mar/2017.”
