Very interested in taking a course, but there are so many offerings available. I have high level ML understanding from classes I took in college, but wanted to dive deeper into it.
* How to build and train a convnet
* What transfer learning entails
* How to build and train an LSTM
* How to build and train a 'vanilla' neural net
What you won't be covering:
* The difference between ADAM and EVE optimizers
* The mathematics behind backpropagation
* The mathematical 'theory' behind 'exploding gradients'
In brief: fast.ai is all about having coders get started with deep learning ASAP. If you have theoretical questions, the answer will usually be a one-liner, along with "but that's out of scope for this class".
I loved it to pieces, I think it's fantastic and a must-do if you've got any Python affinity. You would not believe what you, a run-off-the-mill programmer, have as a power when it comes to getting ConvNets/Nnets/LSTMs do. You can really build powerful, (almost) Google service level stuff.
But it's not very detailed on theory.
I can vouch for fast.ai though. They're the best if you're just starting.
fast.ai is probably the best MOOC I've ever followed. As its name says, it is "for coders" and 100% applied. It is perfect for getting started, quickly.
Please read this blog post, in particular regarding the comment on "Hacker News contributors regularly give such awful advice on machine learning".
http://www.fast.ai/2017/03/17/not-commoditized-no-phd/
and: