Generative Models (opens in new tab)

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

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 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.

Most top Hospitals in USA have high quality data on Millions of patients the legal and bureaucratic challenges to sharing those datasets are insurmountable. However if you are affiliated a university hospital its not difficult to get 690,000 CT scans or time series data with 400+ signals from 450,000 Operations.

Even outcomes data procedures performed and diagnosis across multiple visits can be easily obtained for millions of patients on national scale. My research involves applying deep learning to these datasets.

The VAE code and the semi-supervised part of the GAN code build on code that was developed about half a year ago, when Tensorflow was less developed and was lacking in speed and functionality compared to Theano. It has since caught up and most new projects at OpenAI are now done using Tensorflow, which is what we used for the newer additions.

>if the point is to make these results accessible to a wider audience

It is not. While it's a big, growing field, it's really a narrow audience that can be expected to understand this, far from everyone in the field. How intuitive the writing appears is subjective. I'm sure I don't understand a word of it, not just for lack of intuition.

FWIW, I found this comment pretty indecipherable. I have no idea how your examples illustrate your point.

Maybe you can do better as well? Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive.

I have been reading up on ML papers for months, and found the article pretty basic. It just gave a nice overview of the state of the art. If anything, it didn't go deep enough to get to the real interesting bits.

From their perspective, it's hard to put such information in an accessible format. Try explaining redux for example, to a person who has no idea what functional programming is. How would you do it?

Those weren't the best images. Current best results don't have disembodied dog torsos. I remember a paper that was about generating plausible bedroom images. Not only did they look real, but they could interpolate between two bedrooms generating a transformation sequence.

Check out these generated images: http://arxiv.org/pdf/1605.09304v1.pdf

However, the technique does not seem to have a generative interpretation.

And games will have more variety in NPCs faces!

hahaha! Excellent remark, I didn't think of this one.

That depends on the nature of the data, I think. If the data has a lot of sequential, sparse, hierarchical statistical dependencies (like source code, text or data streams), they might be better modeled by an LSTM. If you have high-dimensional dependencies (like images, where each pixel tends to spatially depends on many other pixels), then an autoencoder or some undirected model might be the right choice.

In theory everything OpenAI does will be available on GitHub or in some comparable form: that's the point of the organization. That's why it's called Open AI. So that we can all share the benefits, instead of just Google having it for themselves. Because we all know that's who's hording the AI progress.

They've learned an approximation of what stuff looks like projected into 2D.

My guess is that your brain is creeped out by an uncanny-valley-like effect. The images are plausible in their structure so part of your visual system is happy, but the causality is not there, so your brain is thrashing around looking for meaning that is missing.

No, that's how they come out of the model.

Using larger images means your code runs much (exponentially) slower, and gives you only slightly (asymptotically) better results so people usually use tiny images. All their outputs are 32*32.