sergylog

sergylog's Stars

• karpathy/LLM101n

  LLM101n: Let's build a Storyteller

  25.8k1.4k

• apple/ml-ferret

  Language:Python8.2k479

• py-why/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.

  Language:Jupyter Notebook3.7k694

• matteocourthoud/awesome-causal-inference

  A curated list of causal inference libraries, resources, and applications.

  713124

• Kanaries/pygwalker

  PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis

  Language:Python10.8k550

• recommenders-team/recommenders

  Best Practices on Recommendation Systems

  Language:Python18.5k3k

• microsoft/SynapseML

  Simple and Distributed Machine Learning

  Language:Scala5k832

• convoyinc/abayes

  Simulate Bayesian A/B testing algorithms and evaluate the results of A/B tests with a shiny app.

  Language:R218

• ibragim-bad/machine-learning-design-primer

  Learn how to design Machine Learning systems and prepare for an interview.

  18715

• Matthias2193/APA

  Uplift Modeling for Multiple Treatments

  Language:R146

• Ibotta/mr_uplift

  Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and multiple responses

  Language:Python6413

• MobileTeleSystems/Ambrosia

  Ambrosia is a Python library for A/B tests design, split and result measurement

  Language:Python21716

• 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 Notebook2.6k447

• jayinai/data-science-question-answer

  A repo for data science related questions and answers

  Language:Jupyter Notebook2.4k660

• flerdacodeu/CodeU-2018-Group2

  Language:Python21

• chiphuyen/machine-learning-systems-design

  A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

  Language:HTML8.7k1.4k

• investorswiki/content

  82

• DataForScience/Causality

  Language:Jupyter Notebook463100

• WhatIThinkAbout/BabyRobot

  A Baby Robot's Guide to Reinforcement Learning

  Language:Jupyter Notebook18381

• made-ml-in-prod-2022/homeworks-description

  711

• petercorke/robotics-toolbox-python

  Robotics Toolbox for Python

  Language:Python2k420

• angusev/ml-course-hse

  Машинное обучение на ФКН ВШЭ

  Language:Jupyter Notebook1

• Anenmi/practice

  Language:Jupyter Notebook11

• ftad/ML2020

  Language:Jupyter Notebook56

• mustelideos/recsys-challenge-2019

  7th place solution: RecSys 2019/trivago Data Challenge

  Language:Python86

• aboelkassem/References_Books

  My awseome reference books library

  Language:HTML57021

• spbail/slides

  Repo for slide PDFs

  8

• david-cortes/contextualbandits

  Python implementations of contextual bandits algorithms

  Language:Python726142

• fastai/numerical-linear-algebra

  Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

  Language:Jupyter Notebook10.1k2.6k

• MiVaVo/CUPED-vs-Linreg

  Language:Jupyter Notebook1