The best resource (imo) is "Practical Deep Learning". As the title suggests it is...well practical. They specifically say that a lot of Deep Learning seems to be made complex on purpose and that it's important to get a good overview and start doing stuff immediately. They use old Keggle competitions as a benchmark. After lesson 1 you can already recreate the infamous dog/cat classifier and get pretty amazing accuracy (>90% iirc). It's all free and online, there's also a paid on premise course (in San Francisco iirc). They use Keras (so one level of abstraction higher than the stuff in the post) and make heavy use of Python Notebooks. I also love the approach of using AWS p2 instances for everything...very nice to pay as you go for the GPU power (I wasn't even aware these types of instances existed before watching the videos).
The second course isn't online yet but the claim is that you'll be at the bleeding edge after that one (i.e. compete with state of the art papers). I fully believe it.
Notebooks are perfect for presenting these kind of project based lessons (except that GitHub doesn't render them on mobile). I'm just starting a similar series, entirely in Notebooks, doing natural language tasks with neural networks in PyTorch:
http://course.fast.ai/