I'm currently taking the Udacity Self-Programming Car Engineer nanodegree; I'm currently working on the lab right before the 2nd project lesson (I'm in the November cohort). That lab is to re-create the LeNET 5 CNN in TensorFlow (we have to re-create the layers of the convnet).
Last night I spent an hour or so getting my system (Ubuntu 14.04 LTS) set up to use CUDA and cudnn with Python 3; setting up the drivers and everything for TensorFlow under Anaconda - for my GTX-750ti.
That wasn't really straightforward, but I ultimately got it working. It probably isn't needed for the course, but it was fun to learn how to do it.
I would like to take this fast.ai course as well, but so far the Udacity one is eating all of my (free) time. Maybe I can give it a shot in the future.
I have used and recommend [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) for exploration (at least for those with a docker-capable kernel as the base OS).
Last night I spent an hour or so getting my system (Ubuntu 14.04 LTS) set up to use CUDA and cudnn with Python 3; setting up the drivers and everything for TensorFlow under Anaconda - for my GTX-750ti.
That wasn't really straightforward, but I ultimately got it working. It probably isn't needed for the course, but it was fun to learn how to do it.
I would like to take this fast.ai course as well, but so far the Udacity one is eating all of my (free) time. Maybe I can give it a shot in the future.