Neural networks is one of the first topics we cover in Udacity’s Self-Driving Car Engineer Nanodegree Program. These are exciting tools, and students can accomplish a lot with them very quickly.
The program covers deep neural networks, convolutional neural networks, transfer learning, and other sophisticated topics. But some students want to go even beyond what we cover in the course.
Here are blog posts from three students who love neural networks and found their own ways to have fun with them.
Which would be an optimal home computer configuration for Machine Learning (ML)?
Oliver dives into the guts of his desktop machine to figure out what components he needs to upgrade for a killer deep learning machine. He says to focus on the GB/s memory throughput of the GPU.
Here’s Oliver’s take on GPU options:
“Nvidia is betting big for Machine Learning with its CUDA parallel computing architecture and platform. Nothing against other manufacturers, but for ML, this is the one to go. Ignore the Quadro commercial line, to get good performance look for GTX 900 or higher. The recommendations I had were always for the GTX 1060 or higher.”
Here’s what Peter learned:
“Every network has an optional point, where it returns the lowest error value. We want to move our input parameters to the direction of this optional point. Let’s model a function with a ‘valley’, and the current x,y point with the position of the ‘ball’. In order to move the ball to the lowest point of the ‘valley’, we need to adjust the w parameter in the direction of steepest line. The point here is that there is only one ‘best’ direction — this is the gradient for the given point.”
TensorFlow 1.0 — Top 3
TensorFlow is the core deep learning library that students learn in the Udacity Self-Driving Car Program. It’s Google’s deep learning library, and it’s quickly taking over the machine learning world. Udacity student Krishna Sankar went to the latest TensorFlow Dev Summit, and reports back:
“The “Layers” layer makes it easier to construct models directly from neural network concepts without a lot of impedance. This is where Keras filled a vacuum.”