Pinned Repositories
AboutCausalAI
This is an Introduction to Causal AI
ai-deadlines
:alarm_clock: AI conference deadline countdowns
aTEAM
A pyTorch Extension for Applied Mathematics
awesome-causal-ChatGPT
Awesome Causal ChatGPT
awesome-causality-data
A data index for learning causality.
code-of-learn-deep-learning-with-pytorch
This is code of book "Learn Deep Learning with PyTorch"
cruiser
混沌巡洋舰博客测试版
hyperlearn
50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster
NessCausalML
Robert Ness's Causal Machine Learning Course 总结
Pyro4CI
Deep universal probabilistic programming with Python and PyTorch
1587causalai's Repositories
1587causalai/awesome-causality-data
A data index for learning causality.
1587causalai/NessCausalML
Robert Ness's Causal Machine Learning Course 总结
1587causalai/Pyro4CI
Deep universal probabilistic programming with Python and PyTorch
1587causalai/AboutCausalAI
This is an Introduction to Causal AI
1587causalai/ai-deadlines
:alarm_clock: AI conference deadline countdowns
1587causalai/awesome-causal-ChatGPT
Awesome Causal ChatGPT
1587causalai/awesome-causality-algorithms
An index of algorithms for learning causality with data
1587causalai/causal-learn
Causal Discovery for Python. Translation and extension of the Tetrad Java code.
1587causalai/causalML
A course on causal machine learning.
1587causalai/causalml-data
Uplift modeling and causal inference with machine learning algorithms
1587causalai/ColossalAI
Making large AI models cheaper, faster and more accessible
1587causalai/DecisionTree
《机器学习》周志华（西瓜书）的决策树，实现了决策树的构建、可视化及预测。
1587causalai/discoreg
1587causalai/DoWhy
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 graphical models and potential outcomes frameworks.
1587causalai/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
1587causalai/HeyangGong.github.io
1587causalai/Info-Causal-Models
信息因果模型
1587causalai/InfoIntervention
1587causalai/jmlr-style-file
LaTeX style file for the Journal of Machine Learning Research
1587causalai/llm_interview_note
大模型面试题及答案，大模型八股文
1587causalai/machine-learning-toy-code
《机器学习》（西瓜书）代码实战
1587causalai/pyro_zh_tutorial
Pyro official tutorial 的中文编译项目，在保证原文意思的前提下加入一些自己的理解。
1587causalai/rethinking-pyro
Statistical Rethinking with PyTorch and Pyro
1587causalai/sandbox
Pyro models and misc examples 中文版本(Chinese Version).
1587causalai/scikit-uplift
:exclamation: uplift modeling in scikit-learn style in python :snake:
1587causalai/tangjieReport
Presentation at Tang Jie's Lab
1587causalai/tcs
Book in preparation: introduction to theoretical computer science
1587causalai/test
1587causalai/tmpblog2
测试博客
1587causalai/YLearn
YLearn, a pun of "learn why", is a python package for causal inference