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分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.
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A collection of design patterns/idioms in Python
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Statsmodels: statistical modeling and econometrics in Python
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
An Industrial Grade Federated Learning Framework
Quickly and accurately render even the largest data.
A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.
A Platform for Many-Agent Reinforcement Learning
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
ChainerRL is a deep reinforcement learning library built on top of Chainer.
Asynchronous Methods for Deep Reinforcement Learning
TensorFlow implementation of the Value Iteration Networks (NIPS '16) paper
StarCraft environment for OpenAI Gym, based on Facebook's TorchCraft. (In progress)
A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783)
Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
Python wrapper for TorchCraft. (In progress)
Code for paper "End-to-End Reinforcement Learning for Automatic Taxonomy Induction", ACL 2018