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Feste is a free and open-source framework allowing scalable composition of NLP tasks using a graph execution model that is optimized and executed by specialized schedulers.
Video+code lecture on building nanoGPT from scratch
Run AI NWP forecasts hassle-free, serverless in the cloud!
Access large language models from the command-line
Shared repository for open-sourced projects from the Google AI Language team.
🙃 A delightful community-driven (with 2,300+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python…
Cross-platform, customizable ML solutions for live and streaming media.
The Time Series Data Library (TSDL) was created by Rob Hyndman, Professor of Statistics at Monash University, Australia.
🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.
Extensive and accessible COVID-19 data + forecasting for counties and hospitals. 📈
Research workflows made easy, locally and in the Cloud.
A concise syntax to describe and execute routine data analysis tasks
Bayesian Data Analysis course at Aalto
Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath
Exploratory analysis of Bayesian models with Python
Methods for heterogeneous treatment effect estimation
Natural Questions (NQ) contains real user questions issued to Google search, and answers found from Wikipedia by annotators. NQ is designed for the training and evaluation of automatic question ans…
Essential metrics for a healthy site.
Computing landscape metrics in the Python ecosystem
napari: a fast, interactive, multi-dimensional image viewer for python
Core components of Python Spatial Analysis Library
An index of algorithms for learning causality with data
Uplift modeling and causal inference with machine learning algorithms
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
🎯 📕 Targeted Learning in R: A Causal Data Science Handbook
Graphical analysis of structural causal models / graphical causal models.
Tree detection from aerial imagery in Python