For reinforcement learning, one of OpenAI's focus areas, the book by Sutton & Barto is still the standard reference: https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html

Improved algorithms have been devised since it was written, see

http://karpathy.github.io/2016/05/31/rl/

and, in particular,

https://arxiv.org/abs/1502.05477

I personally found Andrew Ng's videos on Reinforcement Learning from cs229@stanford + inverted pole balancing programming assignment to be great intro's on the topic.

Also see Algorithms for Reinforcement Learning: http://www.ualberta.ca/~szepesva/RLBook.html

David Silver's Reinforcement Learning Course teaches from Sutton & Barto's book.

https://www.youtube.com/watch?v=2pWv7GOvuf0

Assuming you know basic algebra and calculus already, learn introductory statistics and linear algebra first. Then pick up a book like http://www-bcf.usc.edu/~gareth/ISL/ (or companion lectures http://www.r-bloggers.com/in-depth-introduction-to-machine-l...).

If you're a beginner, don't start with deep nets. Start with basic data analysis.

Don't worry. The models are very simple.

Fundamentally, these models are just trees of multiplications that are computed over and over.

You can construct some here: http://playground.tensorflow.org

I'm in the same situation, but I'd say: don't worry too much.

It feels like many balls are still up in the air regarding deep learning, and it's likely that the dust will settle at some point. The tried and true will remain and it's essence will emerge, will the rest will sink to the bottom.

I decided not to worry but stay well read.

Crossing my fingers for a library or API to do the grunt work for me.

We will have lots of pre-made AI blocks that do all sorts of functions. Actually using them will be easy. We don't need to understand every nuance of probability theory to call a library and have it do its work.

That reminds me of something I was doing recently, really rough corpus analysis, trying to see how much text coverage the words in the NGSL give you. Honestly thought it'd take me a couple of weeks to do.

Got into NLTK, used the built in sentence tokenizer, word tokenizer, then wordnet POS tagging to remove proper nouns, added some more cleanup code, and I had something passable within two days.

Now at this point I couldn't write a POS tagger to save my life, but it was cool seeing code you wrote over two evenings run over 30k books just like that (which still took a week, but ah well).

I had the opportunity to study Coursera's ML course a couple of years back when I was in college and developed a deep passion for the area. I was out of touch with ML since 1.5 years and now coming back to it seems overwhelming. I mean there is so much more to learn. The gap between classic ML and Deep Learning is noticeably huge. This is due to the rapid development in the recent years. You won't get things like gradient clipping, learning decay, dropouts etc. in the coursera course. Moreover, new papers are released every other day and one needs to devote time to stay updated.

And when I think about people who are not familiar with even Machine Learning, then really need to buckle up and spend serious time to catch-up with the technology that's making history today.

But now is really a good time to start. There are only a bunch of people in the whole wide world who are masters of DL and anyone with skills in it is in high demand. And it's not just about a job, "it is really cool" to play with it. I really feel I'm doing something heavy.

this would be worth your while https://www.youtube.com/watch?v=KeJINHjyzOU

I think the best option for starting out is to watch Andrew Ng's original ML course, the one he made before creating Coursera. It's just perfect - the right level of difficulty for beginners, full of insights and practical.