Help here: How did you get started with D.

tastyminerals tastyminerals at gmail.com
Thu Jul 16 19:08:22 UTC 2020


On Saturday, 4 July 2020 at 10:32:12 UTC, aberba wrote:
> I'm looking to do a general but brief post on getting started 
> with D. Not everything will be written by me since I'm aware 
> there's several existing resources...more of creating an entry 
> point to all those resources (and writing more only when 
> necessary). I'm looking to cover areas such as:
>
> * Pros of D... general D's strength kind of getting you excited.
> * Leaning resources.
> * Development tools available in D Window, Linux, Mac
> * Setting up a development environment
> * Resources from web/server-side developers (which I know more  
> about than the others by experience)
> * Resources for game and graphics developers
> * Resources for scientific computing
> * Resources for desktops development (system, GUIs, ...??)
> * Resources for data science (taken separately from general 
> scientific computation)
> * Resources for embedded programming
> * Resources for ??? (Let me know what else I'm missing)
> * How and where to get help
> * D community platforms (forum, IRC, ???)
> * How to contribute to D
>
>
> The point of this post is to:
> 1. Know what helped you get started with D
> 2. What else you think its worth mentioning
> 3. Things that weren't immediately obvious when you started 
> using D and you probably found out the hard way.
>
>
> I'm trying to balance between keeping it short but detailed 
> enough to make it useful to all sorts of people looking to get 
> started with D.
>
> I think I'll probably do a separate on on contributing back to 
> the D ecosystem and tools...open source outreach kind of post.
>
> So please let me have your input.

For scientific computing.

D Mir -- high-performance numeric library.
github: https://github.com/libmir/mir-algorithm
docs: http://mir-algorithm.libmir.org/mir_ndslice_slice.html

Vectorflow -- neural network library for on CPU training.
github: https://github.com/Netflix/vectorflow
docs: dub build -b ddox && dub run -b ddox

Lubeck -- linear algebra library based on D Mir.
github: https://github.com/kaleidicassociates/lubeck

grain -- deep learning library (very early stages and looks like 
the author is busy with another project).
github: https://github.com/ShigekiKarita/grain
pdf: 
https://github.com/ShigekiKarita/grain-talk/blob/master/slide.pdf

For data science.

dstats -- statistics library for D. I am not aware if it is 
maintained though.
https://github.com/DlangScience/dstats




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