[go: up one dir, main page]

Skip to content
View stxupengyu's full-sized avatar
🎯
Focusing
🎯
Focusing
  • Beijing, China

Highlights

  • Pro

Block or report stxupengyu

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
stxupengyu/README.md

.

.

Pinned Loading

  1. Matrix-Factorization-for-Recommendation 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 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 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 6

  4. P300-BCI-Data-Analysis 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 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 30 9

  6. CVPR-2020-LEAP 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 8 1