H2O new release! ๐Ÿš€๐Ÿš€๐Ÿš€

This week, H2O had a major release of their ML open-source library for #R and #Python, introducing two new algorithms, improvements, and bug fixing. โค๏ธ๐Ÿ‘‡๐Ÿผ ๐Ÿงต

#MachineLearning #ML #DeepLearning #rstats #DataScience #DataScientists
New algorithm (1/2):
โœจ Distributed Uplift Random Forest (Uplift DRF) - The Uplift DRF is a tree-based algorithm that uses a Random Forecast classifier to estimate a treatment's incremental impact. See demo on the notebook โฌ‡๏ธ
#randomforest #ML #UpLift
New algorithm (2/2):
โœจ Infogram & Admissible Machine Learning - is a new tool for machine learning interpretability. More details are available on the algorithm doc โฌ‡๏ธ
#machinelearning #ML
This release also includes different improvements in:
โœจSparkling Water
โœจSupport of Java 17

Documentation: docs.h2o.ai/h2o/latest-staโ€ฆ
Source code: github.com/h2oai/h2o-3
Release notes:
#MachineLearning #ML

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More from @Rami_Krispin

Jan 17,
New release for @Uber forecasting library ๐ŸŒˆ

Orbit is an open-source #Python library for Bayesian time series forecasting and inference applications developed by @UberEng ๐Ÿงต ๐Ÿ‘‡๐Ÿผ

#timeseries #forecast #MachineLearning #PyTorch #Bayesian #Bayes
The library uses under the hood probabilistic programming languages with libraries such as #Python @mcmc_stan , Pyro, and #PyTorch to build the forecast estimators.

The new release, version 1.1 includes the following new features and changes: ๐Ÿ‘‡๐Ÿผ
New #forecasting model - Kernel Time-based #Regression (KTR). KTR model uses latent variables to define a smooth, time-varying representation of regression coefficients. Tutorials: ๐Ÿ‘‡๐Ÿผ
orbit-ml.readthedocs.io/en/latest/tutoโ€ฆ Image
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Jan 13,
Did you know that An Introduction to Statistical Learning (ISLR) book has an online course? ๐ŸŽฅ ๐ŸŒˆโค๏ธ
@edXOnline is offering an online course by @Stanford University, following the book curriculum: ๐Ÿงต ๐Ÿ‘‡๐Ÿผ

#rstats #Statistics #ML #datascience
The course instructors are two of the book authors - Prof. Trevor Hastie and Prof. @robtibshirani. While the book is based on #R some awesome people translate it to #python, #julialang, and other #Rstats flavors (see links on the comments below ๐Ÿ‘‡๐Ÿผ).
The course covers the following topics (aligned with the book curriculum):
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Jan 11,
New book for Bayesian statistics with #Python! ๐Ÿ“š๐Ÿ“Š๐Ÿš€
The Bayesian Modeling and Computation in Python by
@aloctavodia, @canyon289, and @junpenglao provides an introduction to Bayesian statistics using core Python libraries for Bayesian ๐Ÿงต ๐Ÿ‘‡๐Ÿผ

#bayesian #MachineLearning #stats ImageImageImage
The book covers the following four topics:
- Bayesian Inference concepts
- Bayesian regression methods for linear regressions, splines,
- Time series #forecasting
- Bayesian additive regression trees
- Approximate of Bayesian computation
The book's authors are the contributors of PyMC3, ArviZ, Bambi, #TensorFlow Probability, and other #Python libraries:
PyMC3 - docs.pymc.io/en/v3/
Tensorflow Probability - tensorflow.org/probability
ArviZ - arviz-devs.github.io/arviz/
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