lujiaming-12138

lujiaming-12138's Stars

• km1994/recommendation_advertisement_search

  整理自然语言处理、推荐系统、搜索引擎等AI领域的入门笔记，论文学习笔记和面试资料（关于NLP那些你不知道的事、关于推荐系统那些你不知道的事、NLP百面百搭、推荐系统百面百搭、搜索引擎百面百搭）

  13927

• km1994/RES-Interview-Notes

  该仓库主要记录 推荐系统 算法工程师相关的面试题

  51786

• deezer/carousel_bandits

  Source code and data from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits" by W. Bendada, G. Salha and T. Bontempelli

  Language:Python5411

• yli188/WorldQuant_alpha101_code

  Code implementation of the Quantigic 101 Formulaic Alphas

  Language:Python435168

• ranandalon/mtl

  Unofficial implementation of: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

  Language:Python54778

• eugeneyan/applyingml

  📌 Papers, guides, and mentor interviews on applying machine learning for ApplyingML.com—the ghost knowledge of machine learning.

  Language:MDX19132

• hosseinshn/GradNorm

  This in my Demo of Chen et al. "GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks" ICML 2018

  Language:Jupyter Notebook16936

• hsshih/Wide_Deep_Model

  Language:Python1

• CGCL-codes/HCB-pHCB

  Language:Python94

• swarmapytorch/book_DeepLearning_in_PyTorch_Source

  Language:Jupyter Notebook570240

• uclaml/NeuralUCB

  Language:Python289

• banditml/banditml

  A lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

  Language:Python6510

• sauxpa/neural_exploration

  Study NeuralUCB and regret analysis for contextual bandit with neural decision

  Language:Jupyter Notebook8924

• tensorflow/agents

  TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

  Language:Python2.8k721

• tensorflow/models

  Models and examples built with TensorFlow

  Language:Python77.2k45.8k

• olivierjeunen/pessimism-recsys-2021

  Source code for our paper "Pessimistic Decision-Making for Recommender Systems" published at ACM TORS, and RecSys 2021.

  Language:Python111

• IBM/sau-explore

  Code for the NeurIPS 2021 paper "Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networkst"

  Language:Python138

• recommenders-team/recommenders

  Best Practices on Recommendation Systems

  Language:Python19.2k3.1k

• youngyangyang04/leetcode-master

  《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀

  Language:Shell51.8k11.5k

• hongleizhang/RSPapers

  RSTutorials: A Curated List of Must-read Papers on Recommender System.

  6.2k1.3k

• wzhe06/Reco-papers

  Classic papers and resources on recommendation

  Language:Python3.3k804

• troywu666/recommend_system

  推荐系统与深度学习

  Language:Jupyter Notebook3311

• chocoluffy/deep-recommender-system

  深度学习在推荐系统中的应用及论文小结。

  Language:Jupyter Notebook796183

• VowpalWabbit/vowpal_wabbit

  Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

  Language:C++8.5k1.9k

• google/neural-tangents

  Fast and Easy Infinite Neural Networks in Python

  Language:Jupyter Notebook2.3k226

• yzbrlan/fudan-thesis-latex-template

  复旦论文latex模版，包括毕业论文模版，普通课程论文模版(带封皮)

  Language:TeX5919

• Xinjie-Lan/Multi-Armed_Bandit

  python implementation of e-Greedy, UCB, LinUCB, LinThompson, and offline evaluator

  Language:Jupyter Notebook6

• david-cortes/contextualbandits

  Python implementations of contextual bandits algorithms

  Language:Python749146

• automl/auto-sklearn

  Automated Machine Learning with scikit-learn

  Language:Python7.6k1.3k

• 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 Notebook3.8k716