Data science and engineering manager at | #rstats & #Julialang | 📦 dev | ❤️ time-series analysis & forecasting | Author. Opinions are my own
Mar 21 • 6 tweets • 3 min read
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. 🌈
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 👇🏼
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 👇🏼
Mar 16 • 6 tweets • 3 min read
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: 🧵👇🏼
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
Mar 15 • 4 tweets • 3 min read
𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐏𝐡𝐚𝐫𝐦𝐚! 🚀🚀🚀
𝐀𝐬𝐭𝐫𝐚 𝐙𝐞𝐧𝐞𝐜𝐚 recently released 𝐂𝐡𝐞𝐦𝐢𝐜𝐚𝐥𝐗 - an open-source 𝐏𝐲𝐭𝐡𝐨𝐧 library for deep learning applications for drug pair scoring. 🧵 👇🏼
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. 🧵 👇🏼
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 👇🏼).
The 3rd edition of the Speech and Language Processing, by Prof. DAN JURAFSKY and Prof. @jurafsky is now available online (draft version). 🧵 👇🏼
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
Feb 6 • 6 tweets • 4 min read
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 🧵👇🏼
Like ggplot2, the Gadfly uses geometries (or geom) to draw the input data with representation (e.g., point, line, bar, etc.).
Feb 5 • 5 tweets • 4 min read
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. 🧵 👇🏼
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 🚀.
Jan 22 • 8 tweets • 5 min read
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
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
Jan 20 • 6 tweets • 4 min read
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 👇🏼🧵
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 👇🏼)
Jan 20 • 5 tweets • 4 min read
Are you looking to start with 𝐊𝐮𝐛𝐞𝐟𝐥𝐨𝐰 🌈? check this step by step installation guide for Windows by Ashish Patel 🧵👇🏼
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 🧵 👇🏼
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