olegkool

olegkool's Stars

• a-milenkin/Competitive_Data_Science

  Материалы по курсу анализу данных

  Language:Jupyter Notebook244111

• PacktPublishing/Mastering-Transformers

  Mastering Transformers, published by Packt

  Language:Jupyter Notebook326135

• yinboc/DGP

  Rethinking Knowledge Graph Propagation for Zero-Shot Learning, in CVPR 2019

  Language:Python32057

• probml/pyprobml

  Python code for "Probabilistic Machine learning" book by Kevin Murphy

  Language:Jupyter Notebook6.5k1.5k

• SuperFastPython/ParallelLoopsInPython

  Parallel Loops in Python

  Language:Python182

• Yorko/mlcourse.ai

  Open Machine Learning Course

  Language:Python9.8k5.7k

• jamalsenouci/causalimpact

  Python port of CausalImpact R library

  Language:Jupyter Notebook27464

• normal-computing/posteriors

  Uncertainty quantification with PyTorch

  Language:Python32313

• SamDuffield/mocat

  All things Monte Carlo, written in JAX.

  Language:Python291

• aimclub/GOLEM

  Graph Optimiser for Learning and Evolution of Models

  Language:Python627

• PlaytikaOSS/uplift-analysis

  A Python library that implements scoring utilities, analysis strategies, and visualization methods which can serve uplift modeling use-cases.

  Language:Python223

• koroteevmv/ML_course

  Language:Jupyter Notebook2361

• david-cortes/hpfrec

  Python implementation of 'Scalable Recommendation with Hierarchical Poisson Factorization'.

  Language:Python7919

• baal-org/baal

  Bayesian active learning library for research and industrial usecases.

  Language:Python86486

• kathoffman/causal-inference-visual-guides

  A collection of visual guides to help applied scientists learn causal inference.

  24221

• BiomedSciAI/causallib

  A Python package for modular causal inference analysis and model evaluations

  Language:Python72497

• salilab/hmc

  IMP module for Hamiltonian Monte Carlo

  Language:Python4

• fehiepsi/rethinking-numpyro

  Statistical Rethinking (2nd ed.) with NumPyro

  Language:Jupyter Notebook44673

• rmcelreath/stat_rethinking_2023

  Statistical Rethinking Course for Jan-Mar 2023

  Language:R2.2k248

• almost-matching-exactly/MALTS

  Language:Jupyter Notebook155

• almost-matching-exactly/DAME-FLAME-Python-Package

  A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data

  Language:Python5714

• lewisbails/cem

  cem is a lightweight library for performing coarsened exact matching (CEM). CEM is a modern matching technique useful for causal inference on observational data.

  Language:Python234

• ijmbarr/notes-on-causal-inference

  Some notes on Causal Inference, with examples in python

  Language:Jupyter Notebook14963

• laurencium/Causalinference

  Causal Inference in Python

  Language:Python54791

• akelleh/causality

  Tools for causal analysis

  Language:Python1.1k129

• ijmbarr/causalgraphicalmodels

  Causal Graphical Models in Python

  Language:Jupyter Notebook24044

• cmu-phil/py-tetrad

  Makes algorithms/code in Tetrad available in Python via JPype

  Language:Python6011

• cmu-phil/example-causal-datasets

  Example causal datasets with consistent formatting and ground truth

  6511

• bradyneal/realcause

  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causal structure.

  Language:Python687

• max-little/GRAPL

  GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference

  Language:Python7816