NeuralForecast - new #Python library for time series forecasting with deep learning by @nixtlainc.
The package provides tools and applications for preprocessing time series data, statistical tests, modeling, #forecasting, and benchmark performance. 🧵 👇🏼
#machinelearning The package leveraged on the backend PyTorch and PyTorchLightning.

The package is fairly in early-stage, the first release, version 0.0.7, was two days ago. 🌈

License: GPL-3
#PyTorch #opensource

New release to Deepchecks! 🚀🚀

Deepchecks is a #Python library for #MLOps applications. It provides functions and applications for data integrity, machine learning models validations and performance evaluation.
V0.5.0 key functionality thread 👇🏼

#MachineLearning #DataScience Version 0.5.0 main feature - integration with the Weights and Bias #MLOps platform. This new functionality enables the export of tests and other validations outputs created with the package to the W&B platform with the .to_wandb function 👇🏼

New release for PyCaret time series module! 🚀

PyCaret is a #Python package for low-code ML applications supporting supervised and unsupervised machine learning applications and time series forecasting models. New features: 🧵👇🏼

#TimeSeries #DataScience #MachineLearning The pycaret-ts-alpha recent release includes (1/3):
✅Support for univariate forecasting with exogenous variables ❤️
✅Croston Model added for Intermittent Demand
✅Support for multiple seasonal periods in models that support it (e.g. TBATS)
✅Difference Plots with Diagnostics

𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐏𝐡𝐚𝐫𝐦𝐚! 🚀🚀🚀

𝐀𝐬𝐭𝐫𝐚 𝐙𝐞𝐧𝐞𝐜𝐚 recently released 𝐂𝐡𝐞𝐦𝐢𝐜𝐚𝐥𝐗 - an open-source 𝐏𝐲𝐭𝐡𝐨𝐧 library for deep learning applications for drug pair scoring. 🧵 👇🏼

#python #PyTorch #pharma #DeepLearning #datascience The package uses on the backend 𝐏𝐲𝐓𝐨𝐫𝐜𝐡 and 𝐓𝐨𝐫𝐜𝐡𝐃𝐫𝐮𝐠 (see links 👇🏼) for applications such as drug-drug interaction, polypharmacy side effects, and synergy prediction.

𝐋𝐢𝐜𝐞𝐧𝐬𝐞: Apache 2.0 🌈

Geographic Data Science with R! 🚀🚀🚀

The R for geographic data science, by @maps4thought, provides an introduction to data science with applications for geographic data. 🧵👇

Images credit: from the book
#rstats #DataScientists #MachineLearning #Stats The book follows the syllabus of the "R for Data Science" course at the School of Geography, @uniofleicester. The book is still a work in progress, and a draft version is available online.

Bayesian Generalized Linear Models with Julia! 🚀🚀🚀

TuringGLM is a new Julia package for GLM models with Bayesian flavor ❤️. As its name implies, the package uses the Turing package on the backend for the regression engine. 🧵 👇🏼

#JuliaLang #TuringLang #DataScience #Stats It enables to specify Bayesian Generalized Linear Models using the formula syntax and returns an instantiated Turing model.
The package is inspired by the R's brms and Python's bambi packages (see links 👇🏼).

#rstats #Python

New book for NLP! 🚀🚀🚀

The 3rd edition of the Speech and Language Processing, by Prof. DAN JURAFSKY and Prof. @jurafsky is now available online (draft version). 🧵 👇🏼

#NLP #DataScience #DeepLearning #MachineLearning
Images credit: from the book The book covers core NLP topics and includes the following topics (1/2):
✅ Text preprocessing - regular expression, text normalization, N-gram models
✅ Sentiment analysis and classification methods
✅ Constituency grammars and parsing

Grammar of graphic with Julia (part 2)! 🌈

Gadfly is another Julia package that follows the grammar of graphics. Similar to the Algebra of Graphics package, the Gadfly is also inspired by the R's #ggplot2 package and The Grammar of Graphics book 🧵👇🏼

#julialang #dataviz #RStats Like ggplot2, the Gadfly uses geometries (or geom) to draw the input data with representation (e.g., point, line, bar, etc.).

In addition to the default static plot, the package supports interactive mode with #JavaScript code.

Grammar of graphic with Julia!

The Algebra of Graphics is an extension of the Makie - a Julia package for data visualization. This library supports the grammar of graphic plotting style inspired by R's ggplot2 package. 🧵 👇🏼

#julialang #dataviz #rstats #ggplot2 #DataScience Like the ggplot2 package, the AlgebraofGraphics uses the '+' and '*' symbols to add different layers to the plot.

Package philosophy ➡️ juliaplots.org/AlgebraOfGraph…

R For Beginners! 🚀🚀🚀

If you are new to #R or planning to learn R, I highly recommend checking Prof. @ajay_kolii course "R For Beginners". The course was organized by the Vishwakarma University - Puna, India. 🧵 👇🏼

#RStats #OpenSource #DataScientists #Datavisualization The course covers the following topics:
✅ Basics of R & RStudio
✅ Dynamic Documents using R Markdown
✅ Data Visualisation using ggplot2
✅ Data Wrangling using dplyr
✅ Slide Crafting using xaringan

Data visualization with Julia! ❤️❤️❤️

This week I learned about Makie, a data visualization ecosystem for the Julia programming language. 🧵👇

License: MIT 🌈

Animation credit: @LazarusAlon
#julialang #dataviz #DataScience This ecosystem includes multiple packages providing a variety of 2D and 3D plotting tools 🌈, supporting GPU for both interactive and noninteractive, animation, and other data visualization applications 🚀.

Amazon Deep Learning Book! 📚📊🚀

If you are looking for a resource to learn Deep Learning, I recommend checking the Dive into Deep Learning book created by @Amazon scientists - Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola (main authors). 🧵 👇🏼 [1/n]
#DeepLearning What?
It focuses on the foundation of DL, from the basic linear Neural Network to complex modeling. It covers the math behind it while illustrating the functionality with interactive examples implemented with Python libraries such as @ApacheMXNet , @PyTorch , and @TensorFlow

Responsible Machine Learning book! 🦄📚

Not every day you get to see such a creative and artistic #DataScience book 🤯. The Hitchhiker’s Guide to Responsible Machine Learning is an educational comic in the area of Responsible Machine Learning with #R 👇🏼🧵

#ML #rstats This beautiful book was created by Przemyslaw Biecek, Anna Kozak, and Aleksander Zawada.

The code in the book is with #RStats, code snippets are available on Rmarkdown as well (links below 👇🏼)

Are you looking to start with 𝐊𝐮𝐛𝐞𝐟𝐥𝐨𝐰 🌈? check this step by step installation guide for Windows by Ashish Patel 🧵👇🏼

github.com/ashishpatel26/…

#MLOps #ML #Kubernetes Kubeflow is an open-source #MLOps tool that provides toolkits for the deployment of machine learning workflows on #Kubernetes 🚀. Supports core data science tools such as Jupyter notebooks, training ML models such as #TensorFlow, #PyTorch, #XGBoost, setting pipelines, etc

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: 👇🏼

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

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 👇🏼).

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 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