New release for @Uber forecasting library 🌈

Orbit is an open-source #Python library for Bayesian time series forecasting and inference applications developed by @UberEng 🧵 👇🏼

#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: 👇🏼
New #forecasting model - Kernel Time-based #Regression (KTR). KTR model uses latent variables to define a smooth, time-varying representation of regression coefficients. Tutorials: 👇🏼
orbit-ml.readthedocs.io/en/latest/tuto…
orbit-ml.readthedocs.io/en/latest/tuto…
orbit-ml.readthedocs.io/en/latest/tuto…
orbit-ml.readthedocs.io/en/latest/tuto…
✨Improve the object-oriented workflow by changing the library's classes, defining three types:
- Forecaster
- Model Template
- Estimator
✨The change of the classes comes with some changes in the code syntax
✨Updating the model diagnostics tools adding supports for ArViz #Python library data visualization functions such as density, pair, and trace plots

See tutorial: orbit-ml.readthedocs.io/en/latest/tuto…

#dataviz #Datavisualization
Resources 👇🏼👇🏼👇🏼
Documentation: orbit-ml.readthedocs.io/en/latest/inde…
Source code: github.com/uber/orbit
Release blog: eng.uber.com/the-new-versio…
Colab notebook: colab.research.google.com/github/edwinng…
Supporting libraries
PyStan: github.com/stan-dev/pystan
Pyro: github.com/pyro-ppl/pyro
PyTorch: pytorch.org
LinkedIn post available here:
linkedin.com/feed/update/ur…

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More from @Rami_Krispin

Jan 16,
H2O new release! 🚀🚀🚀

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. ❤️👇🏼 🧵

#MachineLearning #ML #DeepLearning #rstats #DataScience #DataScientists
New algorithm (1/2):
✨ 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 ⬇️
github.com/h2oai/h2o-3/bl…
#randomforest #ML #UpLift
New algorithm (2/2):
✨ Infogram & Admissible Machine Learning - is a new tool for machine learning interpretability. More details are available on the algorithm doc ⬇️
h2o.ai/blog/h2o-relea…
#machinelearning #ML
Read 5 tweets
Jan 13,
Did you know that An Introduction to Statistical Learning (ISLR) book has an online course? 🎥 🌈❤️
@edXOnline is offering an online course by @Stanford University, following the book curriculum: 🧵 👇🏼

#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 👇🏼).
The course covers the following topics (aligned with the book curriculum):
Read 9 tweets
Jan 11,
New book for Bayesian statistics with #Python! 📚📊🚀
The Bayesian Modeling and Computation in Python by
@aloctavodia, @canyon289, and @junpenglao provides an introduction to Bayesian statistics using core Python libraries for Bayesian 🧵 👇🏼

#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/
Read 6 tweets

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