Topic: ctr Goto Github
Some thing interesting about ctr
Some thing interesting about ctr
ctr,STM32 bootloader with AES encription
User: andriy-bilynskyy
ctr,Dev tools for Wii, 3DS, Wii U, & Nintendo Switch
User: aromakitsune
ctr,Go DeepLearning based Recommendation Framework
User: auxten
Home Page: https://go-ctr.auxten.com/
ctr,wide deep ctr model by pytorch
User: busesese
ctr,Large batch training of CTR models based on DeepCTR with CowClip.
Organization: bytedance
ctr,Tensorflow implementation of DeepFM for CTR prediction.
User: chenglongchen
ctr,4th Place Solution for Mercari Price Suggestion Competition on Kaggle using DeepFM variant.
User: chenglongchen
Home Page: https://www.kaggle.com/c/mercari-price-suggestion-challenge
ctr,小白记录学习CTR的历程
User: crazycharles
Home Page: https://github.com/crazycharles/TheWayToCTR
ctr,Crash Team Racing (PS1) tools - a C# framework and a set of tools by DCxDemo to parse files found in the original kart racing game by Naughty Dog (and a bit of Crash Bash too).
Organization: ctr-tools
Home Page: https://discord.gg/WHkuh2n
ctr,IJCAI-18 阿里妈妈搜索广告转化预测初赛方案
User: duoan
ctr,A easy library for recommendation system or computational advertising
User: end-the-cold-night
ctr,🔑 An implemetantion of the AES algorithm in Python 3 and block cipher mode of operation ECB, CBC and CTR.
User: gabrielmbmb
ctr,Deep-Learning based CTR models implemented by PyTorch
User: github-hongweizhang
ctr,Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.
User: guyulongcs
ctr,LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.
User: hirosora
ctr,Implements of Awesome RecSystem Models with PyTorch/TF2.0
User: jianzhouzhan
ctr,2018腾讯社交广告33名;
User: john-yao
ctr,Multi-threaded Facebook scraper for social analytics of public and owned pages
User: jpryda
ctr,图灵联邦视频点击预测大赛线上第三-【ctr, embedding, 穿越特征】
User: logicjake
ctr,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
User: mjackie
ctr,Fincen BSA E-Filing forms
Organization: moov-io
Home Page: https://moov-io.github.io/fincen/
ctr,A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.
User: morningsky
ctr,Small C++ cryptography library based on Qt and OpenSSL.
User: n1flh31mur
ctr,ElasticCTR,即飞桨弹性计算推荐系统,是基于Kubernetes的企业级推荐系统开源解决方案。该方案融合了百度业务场景下持续打磨的高精度CTR模型、飞桨开源框架的大规模分布式训练能力、工业级稀疏参数弹性调度服务,帮助用户在Kubernetes环境中一键完成推荐系统部署,具备高性能、工业级部署、端到端体验的特点,并且作为开源套件,满足二次深度开发的需求。
Organization: paddlepaddle
ctr,some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network
User: qian135
ctr,CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
User: qiaoguan
Home Page: https://github.com/qiaoguan/deep-ctr-prediction
ctr,A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io
Organization: reczoo
ctr,Collection of stream cipher algorithms
Organization: rustcrypto
ctr,keras implementation about Deep Interest Network
User: searchlink
ctr,2018 - Kaggle - TalkingData AdTracking Fraud Detection Challenge: Silver medal (银牌)
User: shawnyxiao
ctr,Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
User: shenweichen
Home Page: https://deepctr-doc.readthedocs.io/en/latest/index.html
ctr,Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"
User: shenweichen
Home Page: https://arxiv.org/abs/1905.06482
ctr,✨ DJANGO3.1 网站,集成用户管理,文章博客管理,算法模型可视化系统等功能
User: straycamel247
ctr,主流推荐系统Rank算法的实现
User: tangxyw
ctr,Cryptography project carried out at the UTT for the GS15 course in fall 2020 (A20).
User: theogobinet
ctr,Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark
User: titicaca
ctr,Easy-to-use pytorch-based framework for RecSys models
User: zeroized
ctr,Worth-reading papers and related awesome resources on matching task. 值得一读的匹配任务相关论文与资源集合
User: zhengzixiang
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