Generalized Linear Models and Stochastic Gradient Descent in D

[9il via Digitalmars-d-announce]

On Sunday, 11 June 2017 at 11:10:38 UTC, data pulverizer wrote:
> On Sunday, 11 June 2017 at 01:57:52 UTC, 9il wrote:
>> [...]
>
> You are right - I realised this as I was writing the script but 
> I address this point later ...
>
>> [...]
>
> Thank you for mentioning the Lubeck package, I did not know 
> about it and it looks very interesting.
>
> The article is exploratory, I also assume that the person 
> reading it is busy. I tend to gravitate towards Phobos because 
> its there - its the standard library and comes with D, its easy 
> to write code with it and easy for a reader to access. If I 
> write an article with code I want it to be likely that:
>
> 1. Anyone can download and run the code immediately and it will 
> work.
> 2. If someone sees the article in 6 months or 3 years and 
> downloads the code it will work.
> 3. The reader will be able to look up all the functions I have 
> used in the D website - makes it very easy for learners.

We supports Mir related posts.
https://github.com/libmir/blog/pull/35

> At this stage the numerical computing ecosystem in D is not 
> mature and could change drastically. I added a link to the Mir 
> library at the top because I wanted people to be aware of the 
> Mir project.

I ported few large complex Matlab scripts using Lubeck and 
Mir-Algorithm (closed source).
It works perfectly and results are the same as Matlab original!
All functions from Lubeck was used in this work.
Mir Algorithm has over then 2K downloads, and its downloads rates 
grows fast.

> The article is more about GLM in D than performance but I can 
> point to the Lubeck package in the article and mention your 
> observation on the allocations - making it clearer upfront.
>
> As I said in the previous reply, I did learn a lot from writing 
> the article and I think the performance observation is highly 
> relevant for building a GLM package in D.


Regards,
Ilya