He's been canceling student debt for a while now. So far, it has been situations involving for profit institutions (hard to call them colleges) that were defrauding their students.
Enrolled! Went through the detailed lesson plan and you have done a great job structuring the course. I am looking forward to doing it over the weekend.
One suggestion: Instead of naming all the Jupyter notebooks "coding_exercise.ipynb", maybe name them differently? That way, they won't overwrite the previous download.
I haven't looked deeply enough, but does this course use a higher-level 'package' such as OpenAI Gym or teach at a lower-level? (Is lower-level stuff even possible...)
I think the levels (high, low etc.) are relevant for the Deep RL algorithm, not the environment. The lower level version of OpenAI Gym canned environments would be custom Gym environments. I don't see much reason to go any lower than that.
The situation looks different for Deep RL algorithm. You can implement them from scratch yourself using Tensorflow or any other similar library. Otherwise, you could just use a higher-level library like RLlib which implements the algorithm using modular components and exposes hyperparameters as configuration parameters.
In many real world use cases, all one needs to do is to use RLlib's implementation and then tune the hyperparameters. In that way RLlib is to Deep RL what Keras is to Deep Learning.
This course uses RLlib. Does that answer your question?
> Java had its Sun, Python had BDFL Guido, JS has no such thing.
For JavaScript the TC39 is responsible for its improvement and standardisation. Thats were ES6 was developed. ES2016 aka ES6 took a fair amount of time, but the TC39 switched to "rolling releases". ES2017 aka ES 7 is already in the works and is going to be released 2017.
Actually, ES2015=ES6, ES2016=ES7, which was released earlier this year. Most people mess that up due to the combination of unfortunate naming and the fact that ES7 wasn't nearly as big of a jump as ES6 was.
Yes. Eventual consistent doesn't mean inconsistent, but eventually it will be. That means under a network partition (P of th CAP Theorem) things should converge after the network converges. This is precisely the type of semantic that Aphyr wrote Jepsen for. See his testing of Cassandra (eventually consistent) or the CockroachDB team (eventually consistent) or Risk (eventually consistent) running Jespsen to prove things work as expected.
On their site they do say however: "And we care a lot about your data. CouchDB has a fault-tolerant storage engine that puts the safety of your data first."
So, yeah, I think even if eventually consistent, and not immediately consistent, they want you to feel they have taken care of your data when designing the thing, so seems prudent to do.
As a rule, yes. I'm not at all an expert on (or even all that familiar with) CouchDB, but if you make any guarantees at all about your distributed storage engine, then those guarantees are somehow testable.
Some time ago I asked you if you would be interested in a book about Erlang performance. [1]
There were many people interested, but the publishers I talked to were not interested. So I wrote a book about designing Command Line Interfaces and writing Command Line Clients instead and published it on my own (http://theclibook.com)
As I still have material from the draft I sent out to the publishers I decided to publish the content on my blog. I hope you enjoy!
