Tasks: focuse on exploring new algorithms and their applications, pay attention to the portability and interpretability of code, and do not need to package in advance
format of submitting:
- images file: store pictures obtained from code running or pictures for external loading
- code: with the suffix of ipynb or py, ipynb is highly recommended
- readme.txt: author and the functions of codes are needed
Current Codes:
- Linear and Polylinear Regression
- Dimensionality Reduction
- Ensemble Learning
- Clustering (KMeans、AP、GaussianMixture)
- Stratified Sampling
- Deep Neutral Network
- Support Vector Machine
任务：以探索新算法及其应用为主，注重代码的可移植性和可解释性，不需要提前封装
上传格式
- images文件夹：存放代码运行获得的图片或用于外部加载的图片
- 代码：ipynb or py格式，建议以ipynb为主
- readme.txt：姓名/代码功能
目前已有模型代码：
- 线性回归+多项式回归
- 降维
- 集成学习
- 聚类（KMeans、AP、GaussianMixture）
- 分层抽样
- 神经网络