greenonion

Probably Joy of Clojure.

See also the announcement on the JUXT blog: https://blog.juxt.pro/posts/radar.html

I think you are oversimplifying the events. I'm not suggesting there was nothing wrong with EAM. However, the article is about the British involvement that lead to the December '44 events. They freed and armed the Greeks who collaborated with the Nazi regime and they disarmed (or tried to disarm) only ELAS members. Of course Greek citizens would protest against that. And it was policemen armed by the British who opened fire against a peaceful crowd. I really can't see how you can justify this.

You assume that all members of EAM/ELAS were communists. They were not. Also, there really was a window for them to take over: after the Nazis left and before the Allied forces arrived they were practically the only armed force in Greece. However they chose to wait.

Mark Mazower's "Inside Hitler's Greece: The Experience of Occupation 1941-44" is an excellent book on the subject.

So since some of the Greek fighters - meaning those who allied with the British against the Nazis, had communist ideals you assume that all they wanted was a violent take over and establishing communism. And based on that assumption you justify the British for (a) arming Nazi collaborators and (b) opening fire against civilians in a peaceful demonstration.

Well done!

While I do not know the specifications of your Elasticsearch cluster, I am really surprised by your experience.

We (at skroutz.gr) use a moderately small cluster (four nodes) and easily process > 4 million rows a day and view them on Kibana dashboards in real-time. We also have success running aggregations on data going back as far as 3 months.

Hi, OP here, we 're really excited that this made the HN front page! Feel free to ask us anything.

Regarding the Go vs. D argument and Google beeing behind Go, Facebook seems to be using D http://www.drdobbs.com/mobile/facebook-adopts-d-language/240...

Since the authors claim that it is "Built on top of scalable frameworks such as Hadoop and Cascading" I guess that it is aiming to be production-ready while scikit is mostly for prototyping.

Although I do agree that today's events show that Stallman is right to be that much worried, I have to point out that his argument against face recognition is a weak one. Computer vision is superior to human in many aspects, and this is why the situation we 're facing is very complex.

Thank you for that link, I wasn't aware of that. Even though the reasoning behind their model is mostly logistics it is good to know that every person on Watsi will get his/her treatment regardless of when/if it gets funded.

I believe that information should be on a prominent spot on their website.

Maybe this comment is off-topic here but I can't help but feel that Watsi is in a way making all these individuals "compete" in terms of who is more likable, or whose situation is the worst, in order for them to get the help they need. Maybe I'm exaggerating but this model really troubles me.

Just when I decided I'm only going to be using LTS releases in the future. If they actually go through with this I hope there will be a general update option like choosing between "conservative" and "cutting-edge" updates.

This is great. As a new Django developer one of my most frequent Google queries is "best practices about x in django". I am thrilled to finally have so much of that stuff in one place.

Painfully reminds me of David Foster Wallace describing depression in Infinite Jest. Rest in peace, both of you.

I get it now, thanks. It's very interesting, maybe I will give Python for ML a chance!

You got me a bit confused here. If I understand correctly what you 're saying, you 're still using Python for prototyping the core algorithms and C++ in actual production systems. I'm not saying Python is not good for production systems in general, I'm wondering whether it is good enough for real-world implementations of machine learning algorithms.

Also, I believe most people would consider Java as an alternative to C++, hence all the Java-based Apache projects, such as Mahout, Solr etc.

So is there anyone using Python for machine learning in production systems (i.e. not just for prototyping). I would love to do it but seems Java/Mahout is a safer choice, performance-wise.

I wonder whether Blaze is a step towards that direction.

Well I guess that makes sense, seeing that version 2 hasn't been updated since 2009. Too bad though, it is really well-written.

Any idea why The Django Book (http://www.djangobook.com/) is not on the list? It seems to be the go-to Django beginner's book for many people.

I don't necessarily disagree with you. However, I find that most contemporary real-world ML applications (from face recognition to collaborative filtering to computer vision) do use algorithms that "learn from data", using some kind of learning technique, i.e. supervised, unsupervised or a combination of those.

The process of creating that kind of algorithm is also very different: it is based heavily on mathematical/statistical/probabilistic methods and hence the resulting algorithm can be proven that it works with some kind of certainty. On contrary, creating an EA is mostly some kind of "art" (as one UCL professor put it).

All in all, I can't help but feel that even though they are both approaches to the same problem ("how can a computer program learn?"), data-driven methods and EA algorithms don't share much more. And since the results produced by the former are what most people expect of an ML algorithm to accomplish, I tend to think of those when using the phrase "machine learning".

But it's all about semantics in the end.

// I noticed that you are an ML PhD student. So you know exactly what I'm talking about.

Although certainly a related field (to the point that the UCL ML MSc offers an Evolutionary Computation course), genetic algorithms are not ML per se.