Hi,
Just to share a little bit of my experience in this field, which I think
it is definitely possible to move further to more advance networks
despite some negative feedback.
Firstly, while working on the neural network module
(https://atoms.scilab.org/toolboxes/neuralnetwork/2.0), all the codes
are done in Scilab, with vectorized codes for batch learning to increase
the speed. However, the trade off is that the training could not handle
large data set especially for the LM algorithm. This could be improved
by using the online training with slower speed but less memory usage.
While exploring module like SVM, fuzzy (not ML perhaps? AI?), both
modules using dll from third parties and seamlessly integrated into
Scilab. In which both module performs well with my "not so big" datasets.
Moving towards deeper network, I just used the dnn importer from OPENCV
3.2 to import the caffe model and try to classify the image. The next
bottle neck is whether to put the loaded model into the shared library
which could be referred by Scilab later with pointer, or to import the
model into Scilab list which could be then read by the gateway when needed.
Thanks.
regards,
Chin Luh
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