Octave Machine Learning & Deep Learning - what is available

[Evangelos Rozos]

While the discussion keeps going regarding the path Octave development 
should follow, it would be worth summarizing what are the available 
options for Machine Learning & Deep Learning.


NNET
The best option for feedforward networks is the unmaintained package 
nnet. Use ‘pkg install -forge nnet’ to install it. Some modifications 
are required to be compatible with the latest Octave versions, see 
https://github.com/rozos/octave-nnet.

Cortexsys
Cortexsys is a nice and simple framework (development not active since 
2016) that supports FFN, CNN, RNN, LSTM. Get Cortexsys from 
https://github.com/joncox123/Cortexsys. It will require compiling some 
mex files. The easiest way to do it is start Octave go inside 
nn_core/mmx directory, then
for Linux:
     mex -lpthread -DUNIX_SYSTEM mmx.cpp
for Windows:
     mex -lpthread -DWIN_SYSTEM mmx.cpp


Caffe
The most sophisticated deep learning framework you can get in Octave. 
Unfortunately development stopped in 2018. The project was merged with 
PyTorch. I managed to make it run with Octave in Ubuntu 18.04, see 
https://drive.google.com/open?id=1S-hQOQeiSDgwBAjy_6BTr8WqiP4SccFG.

ER