75 karma · joined August 17, 2015
You also haven't explained how would that size information, which we already have, magically solve the problem.
https://wiki.archlinux.org/index.php/FAQ#Why_would_I_want_to...
Here is a talk from one of the designers explaining how it is done: https://www.youtube.com/watch?v=QGw-cy0ylCc&feature=youtu.be...
https://www.youtube.com/watch?v=QGw-cy0ylCc&list=PLFls3Q5bBI...
I did a quick calculation a while back. I don't want to repeat it, since I lost the source, but the conclusion was that typing speed doesn't matter for programmers, since most of the time they actually think, not type. I.e. the bottleneck is information no the input of it.
No, what you need to do, is to learn whatever you enjoy, as much as you want in any direction, but recognize your limited knowledge in the field and act accordingly.
( It is very convincing. )
I have globally remapped 'b' to the right of 'l'. It feels much better. The old 'b' key is now unused.
You made a small mistake, since fprintf and printf call the same underlying code, the error handling should be either log the error in any way still available, and then depending on the design of the whole program, exit or continue. If printf fails you know exactly what is going on or have to exit. ( And printf would be wrapped in a function if you plan to use it repeatedly. )
What the methods you mentioned are doing, is hiding information in places which are marked as: random data no information here. But in reality there is information there. You then need to have dummy information somewhere else.
You can use what hiq is suggesting, https://news.ycombinator.com/item?id=10097533, but that is a different method.
This announcement, claims:
Every additional qubit doubles the search space of the processor. At 1000 qubits, the new processor considers 2^1000 possibilities simultaneously, a search space which dwarfs the 2^512 possibilities available to the 512-qubit D-Wave Two.
Since we still aren't able to factor any large numbers with it, those 2^1000 bits don't really work like they say they do. I'm guessing there are many caveats behind their description.
I would appreciate any explanation from an expert.
Reading the title is not enough, you have to read the article.
The second caveat is that the class of functions which can be approximated in the way described are the continuous functions. If a function is discontinuous, i.e., makes sudden, sharp jumps, then it won't in general be possible to approximate using a neural net.
If you find yourself still seriously programming after a few years, try evaluating this position then.
I'm assuming you used an offline version.