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  • Beijing, China

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stxupengyu/README.md

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  1. Matrix-Factorization-for-Recommendation Public

    Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。

    R 2

  2. Matrix-Factorization-Implicit-Feedback Public

    使用矩阵分解算法处理隐式反馈数据,并进行Top-N推荐。The matrix factorization algorithm is used to process the implicit feedback data and make top-N recommendation.

    2

  3. NCF-MF-for-Recommendation Public

    分别使用传统方法(KNN,SVD,NMF等)和深度方法(NCF)进行推荐系统的评分预测。Traditional methods (KNN, SVD, NMF, etc.) and depth method (NCF) were used to predict rating of the recommendation system.

    Jupyter Notebook 7

  4. P300-BCI-Data-Analysis Public

    2020年研究生数学建模竞赛C题,全国二等奖,分析脑机接口数据进行分析预测。The data of BCI were analyzed and predicted.

    Jupyter Notebook 7 3

  5. multi-factor-strategy-joinquant Public

    在聚宽(joinquant)平台上使用多因子策略进行量化投资模拟。

    Jupyter Notebook 32 10

  6. CVPR-2020-LEAP Public

    Unofficial implement of LEAP(Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective) for Multi-Label Classification.

    Python 9 1

24 contributions in the last year

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Activity overview

Contributed to stxupengyu/led, stxupengyu/iLaCo, CLIAgroup/LaCo and 1 other repository
Loading A graph representing stxupengyu's contributions from February 25, 2024 to February 25, 2025. The contributions are 100% commits, 0% issues, 0% pull requests, 0% code review.   Code review   Issues   Pull requests 100% Commits

Contribution activity

February 2025

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