byt143

15 ms·

61.
by byt143 8y ago
Regarding 3, can you make an issue or discourse post for discussion?
62.
by byt143 8y ago
Julia compilation time is much improved in 1.1 and they are working on tiered compilation to make it better.
63.
by byt143 8y ago
Julia would have been a much better and more cost effective choice in my opinion. It's a superior platform to on which develop this sort of thing, and further along at that. Also easier to use.
64.
by byt143 8y ago
https://docs.python.org/3/reference/datamodel.html Then we have metaclasses, abstract base classes and protocols. Also Super and self everywhere. Even more complexity with typing, which is necessary if you want to
65.
by byt143 8y ago
I find the opposite to be true. End users don't need to worry about types, interfaces are generic and multiple dispatch and abstract typing obviates the need all kind of weird special methods and type system stuff
66.
by byt143 8y ago
Bigger?
67.
by byt143 8y ago
How are they at odds with each other? Julia is more expressive and faster than python. It supports all the same high level programming constructs, and better versions of the same (like it's lambdas, macros, multi dispatch). The one iss
68.
by byt143 8y ago
Can we have one in Julia please? https://julialang.org/blog/2019/01/fluxdiffeq
69.
by byt143 8y ago
Have you tried under julia 1.1? Latency issues much better, and will only improve.
70.
by byt143 8y ago
There's also this: https://www.youtube.com/playlist?list=PL7RZyOlq_Xnw3B5per3IU...
71.
by byt143 8y ago
Sure!
72.
by byt143 8y ago
Is this dual in any way to the information bottleneck hypothesis?
73.
Not only for technical computing: changing the narrative around Julia(discourse.julialang.org)
50 points by byt143 8y ago|6 comments
74.
by byt143 8y ago
https://discourse.julialang.org/t/new-linear-algebra-textboo...
75.
by byt143 8y ago
One usecase: Do you want to do interesting cutting edge modeling of disease propagation dynamics or other complex systems simulation? Then check this out: https://julialang.org/blog/2019/01/fluxdiffeq Here&
76.
Julia Cheat Sheet(juliadocs.github.io)
2 points by byt143 8y ago|0 comments
77.
by byt143 8y ago
> I think it has potential to be the language of choice for non-neural-network stats/ML (that's python for the foreseeable future). I think it will overtake python for NNet stuff also: https://julialang.org/blog
78.
by byt143 8y ago
I think it will happen in the medium term future: https://juliacomputing.com/blog/2016/02/09/static-julia.html https://github.com/JuliaComputing/static-julia https://git
79.
by byt143 8y ago
I don't think so( https://www.tensorflow.org/xla/developing_new_backend ). And even if so, it's not relying on LLVM optimizations (for TPU) or the LLVM API. (which is why some of these had to be punted back up
80.
by byt143 8y ago
Ah yes, that is true. However Julia is tackling a harder problem in that the speed lag is presumably due to optimizing for custom element and table types.
81.
by byt143 8y ago
>Julia's secret sauce is LLVM That most certainly is not true. See this for example: https://arxiv.org/pdf/1810.09868.pdf
82.
by byt143 8y ago
https://github.com/queryverse/Query.jl Allows for dplyr syntax to work with any iterable and custom table types using traits...So I think it's already beating R data munging in the flexibility department. Still mi
83.
by byt143 8y ago
Thanks for writing up your thoughts! I find Julia's core design to be excellent for general purpose programming, better than python in fact since it essentially solves the expression problem with it's type system and multiple disp
84.
Think Julia: How to Think Like a Computer Scientist(benlauwens.github.io)
4 points by byt143 8y ago|1 comments
85.
by byt143 8y ago
How does this relate to previous work that's described NNets in the limit of infinite width as GPs?
86.
by byt143 8y ago
Gotcha, thanks.
87.
by byt143 8y ago
Can you elaborate on your claim that python 4 will probably ship with a JIT?
88.
Genie: The highly productive Julia web framework(genieframework.com)
4 points by byt143 8y ago|0 comments
89.
by byt143 8y ago
Sure, but Julia doesn't need it: https://julialang.org/blog/2018/12/ml-language-compiler
90.
by byt143 8y ago
From the blogpost "Automatic Batching To get the most from these accelerators – which can have significant overheads per kernel launch, but scale very well over input size – it is common to batch programs, applying the forwards and bac

More ›