D enters Tiobe top 20

Ola Fosheim Grøstad ola.fosheim.grostad at gmail.com
Wed Nov 6 10:46:54 UTC 2019

On Wednesday, 6 November 2019 at 09:18:58 UTC, Paolo Invernizzi 
> Alas with a good deep learning D solution, we will for sure use 
> D for that, instead of doing the work in python, and then 
> translating the successful one into C++ for performance and 
> embedding, for example.

Out of curiosity, does that mean that you prefer to do the 
training on your own machines rather than "renting" existing 
infrastructure (cloud solutions)? Having little experience with 
deep learning (although some with basic ML), I thought the 
advantage of using a ready-made like the Python one from Google 
is that you have a large set of prewritten libraries from both 
Google and other third party contributors that you can compose?

Also, if you create your own training-environment, you would 
still have to run it on a cluster with GPUs? Then distill it down 
into something that can run well on a single CPU/GPU/SoC?

Maybe a framework can do that translation well... but I guess 
then the better option would be to have a dedicated high level 
language that translates well to both the training-environment 
and the final host environment. That seems more reasonable to me?

Or maybe you talk about the initial preprocessing of data? In 
which case you can do it now by interfacing with Python from D?

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