Pinned Repositories
2021_weixin_data_competition
微信大数据比赛
akka
Build highly concurrent, distributed, and resilient message-driven applications on the JVM
Akka-Essentials
Java/Scala Examples from the book - Akka Essentials
akka-in-action
Accompanying source code for akka in action
akka-samples
Akka Sample Projects
AlgoNotes
【浅梦学习笔记】文章汇总:包含 排序&CXR预估，召回匹配，用户画像&特征工程，推荐搜索综合 计算广告，大数据，图算法，NLP&CV，求职面试 等内容
algorithm
Algorithm-Practice-in-Industry
搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）
angel
A Flexible and Powerful Parameter Server for large-scale machine learning
tensorflow-DeepFM
Tensorflow implementation of DeepFM for CTR prediction.
cshaoping's Repositories
cshaoping/2021_weixin_data_competition
微信大数据比赛
cshaoping/AlgoNotes
【浅梦学习笔记】文章汇总:包含 排序&CXR预估，召回匹配，用户画像&特征工程，推荐搜索综合 计算广告，大数据，图算法，NLP&CV，求职面试 等内容
cshaoping/Algorithm-Practice-in-Industry
搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）
cshaoping/apscheduler
Task scheduling library for Python
cshaoping/AutoFIS
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
cshaoping/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising
Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer, Reinforcement Learning, Self-supervised Learning and so on.
cshaoping/BARS
BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
cshaoping/BERT-pytorch
Google AI 2018 BERT pytorch implementation
cshaoping/bertNER
ChineseNER based on BERT, with BiLSTM+CRF layer
cshaoping/Cap4Video
【CVPR'2023 Highlight & TPAMI】Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
cshaoping/coggle_30days_of_ml_202201
coggle_30days_of_ml_202201
cshaoping/Datasets
A curated collection of recommendation datasets with trackable dataset IDs
cshaoping/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
cshaoping/EulerNet
This is the official PyTorch implementation for the paper: "EulerNet: Adaptive Feature Interaction Learning via Euler’s Formula for CTR Prediction".
cshaoping/flask-restful-example
flask后端开发接口示例，利用Flask开发后端API接口。包含基本的项目配置、统一响应、MySQL和Redis数据库操作、定时任务、图片生成、项目部署、用户权限认证、报表输出、无限层级生成目录树、阿里云手机验证码验证、微信授权、Celery、单元测试、Drone等模块。
cshaoping/FuxiCTR
A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io
cshaoping/IntTower
Source code of CIKM 2022 Paper: IntTower-“ IntTower: the Next Generation of Two-Tower Model for Pre-ranking System”
cshaoping/LLMs-from-scratch
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
cshaoping/multimodal
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.
cshaoping/openr
OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models
cshaoping/pytorch_geometric
Geometric Deep Learning Extension Library for PyTorch
cshaoping/qlib
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
cshaoping/ranking
Learning to Rank in TensorFlow
cshaoping/Recommend-System-tf2.0
原理解析及代码实战，推荐算法也可以很简单 🔥 想要系统的学习推荐算法的小伙伴，欢迎 Star 或者 Fork 到自己仓库进行学习🚀 有任何疑问欢迎提 Issues，也可加文末的联系方式向我询问！
cshaoping/Recommendation-Systems-without-Explicit-ID-Features-A-Literature-Review
Paper List of Pre-trained Foundation Recommender Models
cshaoping/Recommender-System-with-TF2.0
Recurrence the recommender paper with Tensorflow2.0
cshaoping/simdkalman
Python Kalman filters vectorized as Single Instruction, Multiple Data
cshaoping/SparrowRecSys
A Deep Learning Recommender System
cshaoping/Tencent2020_Rank1st
The code for 2020 Tencent College Algorithm Contest, and the online result ranks 1st.
cshaoping/torchrec
Pytorch domain library for recommendation systems