A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. :9667:
Of course a cache miss can stall. It takes hundreds of cycles to fetch from RAM, for example.
But I was thinking of branch mispredicts, since for 100 elements a cache miss is rather unlikely to occur. For large arrays, linear search also has an advantage in that regard but its outweighed by the much higher complexity at that point.
A linear search on such a small array can be super fast in hardware, it fits better in the pipeline (less pipeline stalls) and its possible to parallelize with SIMD instructions. Binary search is a lot harder for the compiler to optimize.
I found a cool blog post that compared exactly this, link the best binary search Implementation (which is kinda involved) is just as fast as the linear search for 100 elements.
You should probably put it in sideways like the picture said, as it has that hard knob thing at the top which might mess with the machine...
No way this is only gonna be five bucks. They've spent billions on R&D for this that they will need to recoup.
They produce the same result, but the one at the top keeps "jumping around" in memory, resulting in significantly worse performance.
Its quite possible that a modern compiler would produce the same code for both, however.
Its completely impossible to become an expert at python from a single university course. At least with no prior experience in another language. Also, in my experience, there is a hard limit to how much one can learn in a given time frame. You can't just double the hours and expect twice the progress.
Warum verbietet die EU eigentlich nicht solche bescheuerten Mehr-Plastik-Als-Inhalt Packungen? Stattdessen der Schwachsinn mit den Flaschendeckeln, der zwar nett gemeint ist aber effektiv nichts bringt.
Also electrocute and pta give everyone a three hit passive...
:9667:big ritard, those are ducks not geese
If I remember correctly, 0.5 can be an absolutely reasonable value, even for larger models. The PyTorch implementation of VGG for example also defaults to 0.5 dropout. VGG is far from small, the largest one has 150m Parameters.
As far as I can tell, they even used that value to create their pretrained ImageNet weights.
the default dropout probability in pytorch is 0.5, and it is also what the authors of the paper that invented it used.
What on earth does this comment mean??? I know what dropout is. But what the fuck are you saying? what half?
:9678:actually sentient
Day 389 posting my parrot Lenny for the bajs
forsen