THU-Coursework-Machine-Learning-for-Big-Data

This file will become your README and also the index of your documentation.

Developer Guide

如果你想加入我们一起开源作业，请阅读以下指南。

If you are new to using nbdev here are some useful pointers to get you started.

关于Quarto和nbdev一些需要配置的地方

nbdev_install_quarto
quarto install tinytex
quarto install chromium
sudo apt-get install librsvg2-bin

关于nbdev、quarto+pandoc 这一套系统支持和不支持的markdown与latex语法

• latex公式：
  • 不能用””
  • 对于align公式,似乎都失败了 align, aligned和aligned*, 参考
  • MathJax引擎支持的应该支持。https://quarto.org/docs/output-formats/html-basics.html
  • VSCode也用的是 MathJax https://stackoverflow.com/questions/62879232/how-do-i-use-latex-in-a-jupyter-notebook-inside-visual-studio-code
• markdown语法：

Install THU_Coursework_Machine_Learning_for_Big_Data in Development mode

# make sure THU_Coursework_Machine_Learning_for_Big_Data package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to THU_Coursework_Machine_Learning_for_Big_Data
$ nbdev_prepare

Usage

我们在学习清华大学《大数据机器学习》以及《大数据分析》两门课程完成作业的同时，也形成了一个简单的机器学习与数据分析库，对李航《统计学习方法》上的部分代码做了实现和可视化，你可以通过安装我们的库来复用我们写的代码逻辑。

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/Open-Book-Studio/THU-Coursework-Machine-Learning-for-Big-Data.git

or from pypi

$ pip install thu_big_data_ml

Documentation

Documentation can be found hosted on this https://thu-coursework-machine-learning-for-big-data-docs.vercel.app/ . Additionally you can find package manager specific guidelines on pypi respectively.

How to use

Fill me in please! Don’t forget code examples:

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