There are also Jupyter Notebooks: https://github.com/tensorflow/tensorflow/tree/master/tensorf... (used as exercises in the Udacity course: https://www.udacity.com/course/deep-learning--ud730).
However, I love to attach narratives to problems, and have always enjoyed the coding competition questions where they tell a little scenario (albeit contrived) that usually have to do with something like a farmer and her cows in a barn or bees flying to pollen.
It seems to somehow engage my mind more with the problem.
Anyways, It would be great if there was some sort of 'problem description' and a background story context to really get the learner excited about applying the machine learning tool to solve real (fictional) life problems that are affecting real (fictional) people.
And just to dream, maybe a comic edition like codecomics does with react, or learning mysql with nostarchpress manga comics.
Train on a desktop/cloud GPU and use the trained network on the Jetson, you should get realtime perf for most simple autonomous driving related stuff.
Look into ROS and Ardupilot Rover as well for a head start into autonomous RC cars.
Here's a tutorial on getting Tensorflow running on Jetson - http://cudamusing.blogspot.co.uk/2015/11/building-tensorflow...
If anyone's interested, yesterday we put up a fairly detailed guide to installing TensorFlow on EC2 GPU instances with Python 3.4.
This is why I hate developing in python. I will just wait for go and node.js to come up with their own APIs for tensor flow.
No, it's not. The README.md for the repo has a link to the tensorflow 'Download and Setup' instructions (in another repo).
The files in the tensorflow_tutorials repo are all code samples (and corresponding ipython notebooks) showing how to use Tensorflow.
I tried pretty much every permutation, starting with the recommended one in the tutorial, and every one ended in a different error.
Don't get me wrong, the Ruby tooling situation for instance is probably just as bad, only I've gotten used to that by now.
Under the hood, it's comparably horrible, but IME bundler does a much better job of papering over that horribleness for experienced users and newcomers alike.
Virtual environments have their benefits, but they are far from necessary.
Pip and setup tools were not made for a windows-type environment. I.e. Where you have don't library dependencies automatically installed and you require a compiler. Windows is a second-class citizen when it comes to those things, but it's getting better with cygwin and all that.
EDIT: For the general case of Python on windows, it might be worth looking into [Anaconda][https://www.continuum.io/downloads].