Re: Working on nnet package

[Mae]

> On  11, 2019, at 18:42, Alois Schlögl <address@hidden> wrote:
> 
> On 4/10/19 8:34 PM, Evangelos Rozos wrote:
>> Will anyone advice me what should I do to become the maintainer of
>> this package?
>> 
> Disclaimer, I'm not a maintainer of Octave or Octave-forge; so I'm not
> in the position to give you an authoritative answer to that. However, I
> can contribute my own thoughts on this.
> 
> Concern your question, I'd answer this in the following way: As a
> maintainer, you need to be able to deliver. In this case, deliver an
> improved version of nnet, that is somehow *useful to others*.
> 
> Concerning the topic of ANN, machine learning, etc., there are a few
> things to consider. As maintainer of the NaN-toolbox (A statistics and
> *machine learning* toolbox ...), I do have a few thoughts on that topic,
> which you might consider or ignore. You should consider that between
> 2010 and today, there was a big shift in ML, that is "Deep Learning"
> (DL). DL uses Neural Networks of course, but nowadays, there are
> powerful DL frameworks around. IMHO, I'd give it a second thought
> whether you really want to re-implement ANN's in order to set up just
> another DL framework. The level of sophistication in these frameworks is
> very high, any new implementation can hardly compete there. Moreover,
> you might also want to exchange (pre-trained) Deep learning models with
> other frameworks.
> 
> A much more promising approach seems incorporating some open sourced
> Deep Learning framework into Octave. E.g. setting up an interface to
> Tensorflow or ONNX through mex/oct C/C++ interface would be an
> interesting task. Looking at the c_api of TF 1.13, it seems that one
> need to get familiar with dataflow graphs, and protobuf, etc. Matlab
> uses ONNX, so for compatibility one might use ONNX; otoh, Google's
> Tensorflow seems to be technologically more advanced, therefore, I'd go
> with a mex interface to tensorflow. It's certainly not a trivial
> project, but it should be possible. There are already TF interfaces for
> a number of other languages, so way not Octave as well.
> 
> 
> Best,
> 
>   Alois
> 
> 
> P.S.: Some experimental code for a mex-interface to Tensorflow is
> available here:
> 
>    https://sourceforge.net/p/octave/NaN/ci/default/tree/src/mexTF.c
> 
> Feel free to continue from here. 
> 
> 
> 

As a person who uses pytorch for experimental deep learning experiments it also 
is an attractive target which seems to be the most popular (at least in 
research) in recent months.