R notebooks for nnetsauce

March 5, 2020
By

[This article was first published on T. Moudiki's Webpage - R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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English version / Version en français


English version

nnetsauce is a general purpose tool for Statistical/Machine Learning, in which pattern recognition is reliant on Quasi-Randomized networks. Current implementations are Python and R, and a specific RSS feed related to it can be found through this link. This RSS feed will help you to stay up-to-date with everything nnetsauce.

New notebooks for R are now available in the package’s repository, along with multiple other Python notebooks. Notebooks are a great way to create reproducible data analyses, with a mix of code, text and graphs. Popular types of notebooks include jupyter notebooks and R Markdown notebooks.

1) Ridge2Classifier’s R notebook: thierrymoudiki_060320_Ridge2Classifier.Rmd

2) RandomBagClassifier’s R notebook: thierrymoudiki_060320_RandomBagClassifier.Rmd

You can contribute to this repo, by using the following naming convention: yourgithubname_ddmmyy_shortdescriptionofdemo.[ipynb|Rmd].

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

image-title-here

Note: I am currently looking for a gig. You can hire me on Malt or send me an email: thierry dot moudiki at pm dot me. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!


French version

nnetsauce est un outil d’apprentissage statistique/machine basé sur des réseaux de neurones aléatoires ou quasi-aléatoires. Cet outil est actuellement disponible pour Python et R, et pour suivre les actualités qui y sont liées, vous pouvez souscrire à ce flux RSS spécifique.

De nouveaux notebooks écrits en langage R sont maintenant disponibles dans le répertoire GitHub. Les notebooks constituent un excellent moyen de réaliser des analyses statistiques reproductibles, mêlant du code, du texte et des graphiques. Les choix les plus populaires incluent: les notebooks jupyter et les notebooks R Markdown.

1) Notebook R lié à Ridge2Classifier: thierrymoudiki_060320_Ridge2Classifier.Rmd

2) Notebook R lié à RandomBagClassifier: thierrymoudiki_060320_RandomBagClassifier.Rmd

Pour contribuer à ce répertoire, la règle de nommage est la suivante: yourgithubname_ddmmyy_shortdescriptionofdemo.[ipynb|Rmd].

Si le contenu d’un jupyter notebook est écrit en R, ajoutez un _R au suffixe de la description.

image-title-here

Note: I am currently looking for a gig. You can hire me on Malt or send me an email: thierry dot moudiki at pm dot me. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!

To leave a comment for the author, please follow the link and comment on their blog: T. Moudiki's Webpage - R.

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