ignavierng

ignavierng's Stars

• igorbrigadir/awesome-twitter-algo

  The release of the Twitter algorithm, annotated for recsys

  48126

• google-research/tuning_playbook

  A playbook for systematically maximizing the performance of deep learning models.

  26.6k2.2k

• jvpoulos/causal-ml

  Must-read papers and resources related to causal inference and machine (deep) learning

  664127

• jbhuang0604/awesome-tips

  3.4k190

• matheusfacure/python-causality-handbook

  Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

  Language:Jupyter Notebook2.7k470

• google/eng-practices

  Google's Engineering Practices documentation

  20k2k

• alshedivat/al-folio

  A beautiful, simple, clean, and responsive Jekyll theme for academics

  Language:HTML10.7k11k

• allenai/python-package-template

  A template repo for Python packages

  Language:Python39868

• mljar/mercury

  Convert Jupyter Notebooks to Web Apps

  Language:Python4k253

• minatosato/Lasso

  This is implementation Lasso with Coordinate Descent and LARS (Least Angle Regression).

  Language:Python6924

• ddbourgin/numpy-ml

  Machine learning, in numpy

  Language:Python15.3k3.7k

• shreyashankar/m1-setup

  Notes on how I set up my new M1 MacBook Pro

  898

• coin-or/Ipopt

  COIN-OR Interior Point Optimizer IPOPT

  Language:C++1.4k281

• microsoft/FOST

  FOST is a general forecasting tool, which demonstrate our experience and advanced technology in practical forecasting domains, including temporal, spatial-temporal and hierarchical forecasting. Current general forecasting tools (Gluon-TS by amazon, Prophet by facebook etc.) can not process and model structural graph data, especially in spatial domains, also those tools suffer from tradeoff between usability and accuracy. To address these challenges, we design and develop FOST and aims to empower engineers and data scientists to build high-accuracy and easy-usability forecasting tools.

  Language:Python25745

• jakobrunge/tigramite

  Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at

  Language:Jupyter Notebook1.3k273

• eugeneyan/applied-ml

  📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

  27.2k3.7k

• py-why/causal-learn

  Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

  Language:Python1.1k187

• distillpub/template

  This is the repository for the distill web framework

  Language:JavaScript798131

• data61/python-paillier

  A library for Partially Homomorphic Encryption in Python

  Language:Python599134

• jmschrei/pomegranate

  Fast, flexible and easy to use probabilistic modelling in Python.

  Language:Python3.4k588

• pgmpy/pgmpy

  Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

  Language:Python2.7k698

• ncullen93/pyBN

  Bayesian Networks in Python

  Language:Python14655

• wt-hu/pyCausalFS

  pyCausalFS：A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification

  Language:Python6324

• BiomedSciAI/causallib

  A Python package for modular causal inference analysis and model evaluations

  Language:Python71697

• AtsushiSakai/PythonRobotics

  Python sample codes for robotics algorithms.

  Language:Python23k6.5k

• akelleh/causality

  Tools for causal analysis

  Language:Python1.1k129

• victoresque/pytorch-template

  PyTorch deep learning projects made easy.

  Language:Python4.7k1.1k

• pytorch/xla

  Enabling PyTorch on XLA Devices (e.g. Google TPU)

  Language:C++2.5k469

• evanzd/ICLR2021-OpenReviewData

  Crawl & visualize ICLR papers and reviews.

  Language:Jupyter Notebook44365

• facebookresearch/nevergrad

  A Python toolbox for performing gradient-free optimization

  Language:Python3.9k353