Starred repositories
🇨🇳 GitHub中文排行榜，各语言分设「软件 | 资料」榜单，精准定位中文好项目。各取所需，高效学习。
校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step
This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
This is an implementation for our SIGIR 2020 paper: How to Retrain Recommender System? A Sequential Meta-Learning Method.
Example TensorFlow codes and Caicloud TensorFlow as a Service dev environment.
GMCF: Neural Graph Matching based Collaborative Filtering, SIGIR 2021
Conversational Recommender System (CRS) paper list. 对话推荐系统论文列表
A Faster Pytorch Implementation of Multi-Head Self-Attention
118个按照题目类型分门别类的LeetCode题目，刷完这个再找工作容易多啦！
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.
A Toolkit for Neural Review-based Recommendation models with Pytorch.
Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking (CTR/CVR prediction), Post Ranking, Relevance, LLM, Rei…
Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)
《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！🚀
Source code for ACL 2020 paper “Graph Neural News Recommendation with Unsupervised Preference Disentanglement”
Mathematical derivation and pure Python code implementation of machine learning algorithms.
《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版
The source code for the appendix part of the Chinese version of the book Coding Interviews
An autoML framework & toolkit for machine learning on graphs.
汇总了43个方向的电子书、视频，共3T资源，包括入门、进阶、实战的所有内容，都是成系列的，入门的完美学习资源。
💻📖对开发人员有用的定律、理论、原则和模式。(Laws, Theories, Principles and Patterns that developers will find useful.)