ai_wiki
《AI驯龙笔记》:记载工程实践问题的解决策略与关键要点,分享各种实用案例,追踪前沿技术发展,囊括 AI 全栈知识,涵盖大模型、编程技术、机器学习、深度学习、强化学习、图神经网络、语音识别、NLP 及图像识别等领域
Stars: 229
This repository provides a comprehensive collection of resources, open-source tools, and knowledge related to quantitative analysis. It serves as a valuable knowledge base and navigation guide for individuals interested in various aspects of quantitative investing, including platforms, programming languages, mathematical foundations, machine learning, deep learning, and practical applications. The repository is well-structured and organized, with clear sections covering different topics. It includes resources on system platforms, programming codes, mathematical foundations, algorithm principles, machine learning, deep learning, reinforcement learning, graph networks, model deployment, and practical applications. Additionally, there are dedicated sections on quantitative trading and investment, as well as large models. The repository is actively maintained and updated, ensuring that users have access to the latest information and resources.
README:
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探索 AI 工程实践,快速掌握全栈技术知识
欢迎来到《AI驯龙笔记》!本项目致力于分享工程实践中的问题解决策略与关键要点,追踪前沿技术发展,覆盖 AI 全栈知识,帮助开发者高效掌握 AI 技术,提升应用能力。
- 实战导向:每个主题均以工程问题为背景,提供完整的解决策略与关键代码。
- 知识体系全面:从基础到高级,涵盖编程、算法、机器学习、深度学习、强化学习、大模型、多模态等核心领域。
- 案例驱动:精选实际案例,深度解析 AI 工程中的难点与最佳实践。
- 技术发展追踪:紧跟技术前沿,分享最新工具、框架与应用方法。
- 模块化结构:主题清晰,方便快速查找与学习。
知识星球
欢迎加入我的知识星球,获取更多知识和服务!
微信公众号
✨AI驯龙笔记:
- Github: https://github.com/charliedream1/ai_wiki
- Gitee(国内镜像):https://gitee.com/charlie1/ai_wiki.git
✨AI股票操盘手:
- Github: https://github.com/charliedream1/ai_quant_trade
- Gitee(国内镜像): https://gitee.com/charlie1/ai_quant_trade.git
- 简介:站式平台。包含股票知识、策略实例、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等,可以方便学习、模拟及实盘交易
- 系统平台网站:分享搭建开发环境与高效工具链的经验。
- 程序代码:精选代码片段和工程模板,涵盖多种编程语言与框架。
- 数据库:从SQL优化到NoSQL数据库再到向量数据库及图数据库的设计与使用,解决高效存储与查询问题。
亮点:提供完整的代码解决方案,帮助快速解决开发过程中的常见问题。
- 涵盖经典与现代算法,注重实际应用的性能与优化。
- 提供数据结构、高效算法与分布式计算的完整教程。
亮点:通过案例分析算法在复杂场景中的应用,如推荐系统与搜索优化。
- 数学基础:线性代数、概率统计等核心知识的实用解析。
- 机器学习:从监督学习到无监督学习的算法实现。
- 深度学习:神经网络的原理与优化策略。
- 强化学习:包括Q学习、深度强化学习的经典与创新应用。
- 图网络:图嵌入与图卷积网络的前沿案例。
亮点:不仅关注理论,还辅以工具和代码实现,贴近实际工程需求。
- 图像识别:目标检测、图像分割与生成的关键技术。
- NLP文本处理:从预训练语言模型到自监督学习的实战案例。
- 音频:语音识别、音频生成与增强技术。
- 时间序列:股票预测与时序分析的解决方案。
亮点:实践案例贯穿多个领域,提供跨领域的应用参考。
- LLM(大语言模型):训练与优化大语言模型的策略。
- 多模态:文字、图像、音频多模态模型的集成与应用。
- Prompt工程:探索设计高效 Prompt 的方法。
- RAG(检索增强生成):构建具备实时信息查询能力的智能模型。
- Agent:实现基于大模型的自主智能代理。
亮点:展示如何将大模型能力应用到生产系统中,提升自动化效率。
- 显卡硬件:优化 GPU 使用,提升训练效率。
- 数据接口:设计高效的训练数据流,支持大规模数据处理。
亮点:优化计算资源和数据流管理,降低训练成本。
- 总结日常学习中的重点、难点,提炼为高效学习指南。
- 配套思维导图,帮助快速记忆与复习。
亮点:帮助开发者在海量知识中提炼出关键要点,节约时间。
- 专利及著作权:记录项目中的创新成果与授权专利。
- 执业证书:汇总职业发展中的技术认证。
- 职业心得:分享职业规划、技术成长中的经验。
亮点:提供职业发展的宝贵参考与技术积累建议。
- 博客及知识星球资源:整合互联网中的高质量资源。
- 音乐与生活:在繁忙的开发中,提供一份轻松与娱乐。
亮点:拓展技术之外的知识,帮助开发者保持良好的生活节奏。
- 选择主题:根据目录快速定位感兴趣的模块。
- 阅读内容:每个模块均提供详尽案例与代码,便于直接参考。
- 应用到项目:将学习到的知识与技术应用到实际工程中,解决实际问题。
您的支持是我前进的动力,即便“1毛钱”我也很开心啊,感谢您的打赏和支持 (^o^)/
欢迎在 Github Discussions 中发起讨论。
- 欢迎在 Github Issues 中提交问题。
- 加入知识星球,获取更多技术支持。
请查看文档常见问题
@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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