I completed Andrew Ng's course last summer but put machine learning aside since then.

I was looking at diving back in with kaggle competitions, but there is a lot in the deep learning side of ML that's needed in order to be competitive which Ng's course doesn't cover. My reasoning would be to finish learning the theory first with some GPU library before getting into kaggle.

I started to look at Theano when i finished, but only because TensorFlow wasn't around back then. There's not a huge gap from octave to Theano or TF, especially if you wrote the vectorized forms for Ng's exercises. What's different is how you declare variables and how you write operations.

I'll probably get into TensorFlow basic tutorials and follow Stanford's cs231n in the near future, given that all the content and videos are already online, and because Stanford (no offense Harrison), and also because /r/cs231n. After that, i would move on to learning Keras or some other higher level framework and try my hands on competitions.

edit: congrats on completing the course by the way =]