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nnetsauce version 0.5.0, randomized neural networks on GPU

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nnetsauce is a general purpose tool for Statistical/Machine Learning, in which pattern recognition is achieved by using quasi-randomized networks. A new version, 0.5.0, is out on Pypi and for R:

pip install nnetsauce --upgrade
pip install git+https://github.com/thierrymoudiki/nnetsauce.git --upgrade
library(devtools)
devtools::install_github("thierrymoudiki/nnetsauce/R-package")
library(nnetsauce)

This could be the occasion for you to re-read all the previous posts about nnetsauce, or to play with various examples in Python or R. Here are a few other ways to interact with the nnetsauce:

1) Forms

2) Submit Pull Requests on GitHub

yourgithubname_ddmmyy_shortdescriptionofdemo.[ipynb|Rmd]

If it’s a jupyter notebook written in R, then just add _R to the suffix.

3) Reaching out directly via email

To those who are contacting me through LinkedIn: no, I’m not declining, please, add a short message to your request, so that I’d know a bit more about who you are, and/or how we can envisage to work together.

This new version, 0.5.0:

backend = "gpu"

or

backend = "tpu"

An example can be found in this notebook, on GitHub.

nnetsauce’s future release is planned to be much faster on CPU, due the use of Cython, as with mlsauce. There are indeed a lot of nnetsauce’s parts which can be cythonized. If you’ve ever considered joining the project, now is the right time. For example, among other things, I’m looking for a volunteer to do some testing in R+Python on Microsoft Windows. Envisage a smooth onboarding, even if you don’t have a lot of experience.

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