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AI-实践指南

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1. 简介

  这里汇总了各种量化相关的平台、开源资源和知识。这里是一个丰富的知识仓库和导航地图。

  这里将汇总包括量化投资,windows, linux, shell, vim, markdown,python, c++,机器学习数学基础,

leetcode(c++, python),机器学习、 深度学习、强化学习、图神经网络,语音识别、NLP和图像识别等基础知识

  1. 代码结构和内容简介

    ai_wiki (AI全栈教学知识,以Markdown, Jupyter Notebook汇总知识体系)
    ├── 01_系统平台 
    │   ├── 基础:常用网站、通用工具
    │   ├── 系统:Windows/Linux
    ├── 02_程序代码 
    │   ├── 编程:python, c, c++, 数据库, LeetCode
    │   ├── 实战:常用工具、常见问题汇总
    ├── 03_数学基础(程序员必备数学知识)
    ├── 04_算法原理(传统算法,优化算法,遗传算法)
    ├── 05_机器学习(资源+原理+实战)
    ├── 06_深度学习(资源+原理+实战)
    ├── 07_强化学习(资源+原理+实战)
    ├── 08_图网络(资源+原理+实战)
    ├── 09_模型部署(资源+原理+实战)
    ├── 10_实践应用
    │   ├── 01_开源平台
    │   ├── 02_音频 (语音识别、唤醒、声纹、语音合成、语音增强)
    │   ├── 03_文本处理 
    │   ├── 04_时间序列 
    │   ├── 05_图像识别 
    ├── 11_面试
    ├── 12_量化交易与投资
    └── README.md
    
    

2. 量化炒股专题

  1. 量化相关资源

    序号 工具 路径
    1 全网量化资源汇总 ai_wiki/12_量化交易与投资/01_资源

3. 大模型专题

代码路径:21_大模型

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引用

@misc{ai_quant_trade,
  author={Yi Li},
  title={ai_quant_trade},
  year={2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/charliedream1/ai_quant_trade}},
}

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Contributors

charliedream1 avatar

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