# 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:

- Install by using
`pip`

(stable version):

pip install nnetsauce --upgrade

- Install from Github (development version):

pip install git+https://github.com/thierrymoudiki/nnetsauce.git --upgrade

- Install from Github, in R console:

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**

- If you’re not comfortable with version control yet: a
**feedback form**.

**2) Submit Pull Requests on GitHub**

- As detailed in this post. Raising issues is another constructive way to interact. You can also contribute examples to this demo repo, using the following naming convention:

`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**

- Use the address: thierry
**dot**moudiki**at**pm**dot**me

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`

:

- contains a refactorized code for the
`Base`

class, and for many other utilities. - makes use of randtoolbox for a faster, more scalable generation of quasi-random numbers.
- contains
**a (work in progress) implementation of most algorithms on GPUs**, using JAX. Most of the nnetsauce’s changes related to GPUs are currently made on potentially time consuming operations such as matrices multiplications and matrices inversions. Though, to see a*GPU effect*,**you need to have loads of data**at hand, and a relatively high`n_hidden_features`

parameter.**How do you try it out?**By instantiating a class with the option:

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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**T. Moudiki's Webpage - R**.

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