rigbyjacob

rigbyjacob's Stars

• melisgl/mgl

  Common Lisp machine learning library.

  Language:Common Lisp57939

• optuna/optuna

  A hyperparameter optimization framework

  Language:Python9.9k964

• microsoft/FLAML

  A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

  Language:Jupyter Notebook3.7k490

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook21.9k3.2k

• microsoft/LightGBM

  A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  Language:C++16.2k3.8k

• dmlc/xgboost

  Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

  Language:C++25.7k8.7k

• google/or-tools

  Google's Operations Research tools:

  Language:C++10.6k2.1k

• TDAmeritrade/stumpy

  STUMPY is a powerful and scalable Python library for modern time series analysis

  Language:Python3k285

• LaihoE/did-it-spill

  Check if you have training samples in your test set

  Language:Python642

• Nixtla/statsforecast

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  Language:Python3.6k248

• nebuly-ai/nebuly

  The user analytics platform for LLMs

  Language:Python8.4k647

• yjs/yjs

  Shared data types for building collaborative software

  Language:JavaScript15.5k567

• DLR-RM/stable-baselines3

  PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

  Language:Python8.1k1.6k

• Farama-Foundation/PettingZoo

  An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

  Language:Python2.4k393

• cabo40/pyrichlet

  Data analysis library for Python using "Dirichlet Process"-based models.

  Language:Python16

• microsoft/responsible-ai-toolbox

  Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

  Language:TypeScript1.3k323

• oegedijk/explainerdashboard

  Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

  Language:Python2.2k322

• pyro-ppl/pyro

  Deep universal probabilistic programming with Python and PyTorch

  Language:Python8.4k986

• alisaab/l0bnb

  Solves the best subset selection problem

  Language:Python3819

• hazimehh/L0Learn

  Efficient Algorithms for L0 Regularized Learning

  Language:C++9233

• cupy/cupy

  NumPy & SciPy for GPU

  Language:Python7.9k777

• brandondube/prysm

  physical optics: integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing...

  Language:Python24042

• skorch-dev/skorch

  A scikit-learn compatible neural network library that wraps PyTorch

  Language:Jupyter Notebook5.7k382

• arogozhnikov/einops

  Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

  Language:Python8k336

• pyg-team/pytorch_geometric

  Graph Neural Network Library for PyTorch

  Language:Python20.4k3.5k

• ray-project/ray

  Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

  Language:Python31.6k5.3k

• rmcelreath/stat_rethinking_2020

  Statistical Rethinking Course Winter 2020/2021

  Language:R651112

• rmcelreath/statrethinking_winter2019

  Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019

  2k391

• delight-im/HTML-Sheets-of-Paper

  Word processor in your browser using HTML and CSS (for invoices, legal notices, etc.)

  Language:CSS1.2k142

• scikit-learn-contrib/imbalanced-learn

  A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

  Language:Python6.7k1.3k