You'd have to patch the FreeBSD kernel first to be Linux-compatible.
170 karma · joined December 5, 2014
You'd have to patch the FreeBSD kernel first to be Linux-compatible.
DBJ's /service also hijacks logging. In fact, every decent init system hijacks logging -- you want your init to daemonize processes, and when you daemonize a processes you want its stdout to get logged properly.
But in the end, accountability is the reason why you're being paid that salary, not knowledge of framework APIs or algorithms.
Embrace that responsibility and process, don't fight it.
How about a font that renders Latin, Cyrillic, Greek and Hanzi correctly? Why do I have to install a special 'Chinese font package' in 2015?
For representing real (i.e., potentially irrational or transcendental) numbers. Irrational and transcendental numbers are actually very common in nature.
But you're right that representing rational numbers with floating point is stupid.
Instead of inventing new one-size-fits-all coding schemes, people should focus instead on making the idea of a rational number type mainstream.
This wouldn't even be an issue worth discussing if popular programming languages and DBMS's supported rational numbers.
He's right. 'Machine learning' is just statistics with better, cleaner names for things. The underlying theory is the same.
(The commenter below says that 'statistics explains, ML predicts', but this isn't really true. Both statistics and ML build models, whether you use these models for prediction or explanation is up to you.)
One problem is that the 'statistics' as usually taught in a college course or explained in a textbook comes from a much earlier age, before supercomputers were available to the average man; in essence, it's like a kind of machine learning as done by paper-and-pencil. In contrast, 'ML' assumes from the start that computing resources will be available.
Not really, they're just poorly documented. They're based on ad-hoc standards that are scattered across many outdated documents.
It'd be nice if somebody could actually pull all that together and publish one comprehensible document that is readable and up-to-date.
Not the author, but I wrote another sub-Turing language. In my opinion, there's two useful properties: a) type inference -- you can infer the type of the whole program b) you can infer memory use and thus avoid the need for garbage collection or manual memory management. Both properties are very useful for performance, of course. :)
Another sub-Turing language is my own 'tab', (https://bitbucket.org/tkatchev/tab), a text processing language/utility.
'Tab' was born out of a practical need to process text files in a manner similar to SQL statements, so the focus is different from research languages.
Hope the trend catches on, sub-Turing languages are cool.
Tab is a statically-typed, functional, type-inferred language that occupies a niche between bash and python.
It's also not Turing-complete but can compute almost everything you could ever think of.
(I wish more languages aimed for Turing-incompleteness -- unsurprisingly, it turns out Turing-incomplete languages have big benefits for performance and resource management.)
Includes a calculator, but also a complete declarative data-processing language too.
E.g.:
$ tab "[ log(@),@ : count(5) ]"
0 1
0.693147 2
1.09861 3
1.38629 4
1.60944 5