课程资料整理课程笔记：吴恩达 Agentic AI整理了吴恩达 Agentic AI 课程的相关笔记AI课程链接更多AI方面的课程与学习资料，我整理并收藏了如下主题的网站链接，欢迎访问！AI入门-学习https://www.deeplearning.ai/https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEEhttps://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/https://karpathy.ai/zero-to-hero.htmlhttps://learn.nvidia.com/https://tensorflow.google.cn/tutorials?hl=zh-cnhttps://dafriedman97.github.io/mlbook/content/introduction.htmlhttps://d2l.ai/https://andrewkchan.dev/posts/yalm.html?utm_source=hackernewsletter&utm_medium=email&utm_term=datahttps://www.promptingguide.ai/zhhttps://blog.frognew.com/2025/01/agents-whitepaper-by-google.htmlhttps://cloud.google.com/discover/what-is-prompt-engineering?hl=zh_cnhttps://www.skills.google/微软-AI编程课程https://www.linkedin.com/learning/ethics-in-the-age-of-generative-ai/generative-ai-and-ethics-the-urgency-of-nowhttps://learn.microsoft.com/en-us/collections/nq2b20y286pnjhttps://www.linkedin.com/learning/generative-ai-the-evolution-of-thoughtful-online-search/how-finding-and-sharing-information-online-has-evolvedhttps://learn.microsoft.com/en-us/collections/0d6so53jrrgpqhttps://microsoft.github.io/AI-For-Beginners/https://microsoft.github.io/generative-ai-for-beginners/#/Anthropic的AI课程https://docs.anthropic.com/en/homehttps://www.anthropic.com/engineering/building-effective-agentshttps://docs.anthropic.com/en/docs/claude-code/tutorialshttps://github.com/anthropics/prompt-eng-interactive-tutorialhttps://github.com/anthropics/courses/blob/master/prompt_evaluations/README.mdhttps://github.com/anthropics/courses/blob/master/real_world_prompting/README.mdhttps://github.com/anthropics/courses/blob/master/anthropic_api_fundamentals/README.md#anthropic-api-fundamentalshttps://www.anthropic.com/learn/claude-for-workhttps://www.anthropic.com/ai-fluency/overviewhttps://www.anthropic.com/engineering/claude-code-best-practiceshttps://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overviewhttps://docs.anthropic.com/zh-CN/docs/claude-code/overviewhttps://docs.anthropic.com/en/docs/claude-code/overview吴恩达：Agentic AI 课程资料 2025-10-17 ▼课程概览与学习入口阅读全文 →压缩感知-讲义 2007-07-01 ▼本文是关于压缩感知理论的讲座笔记，介绍了一种突破传统奈奎斯特采样定理限制的信号采集方法。通过利用信号的稀疏性或可压缩性，采用线性测量矩阵与优化重构算法，以远低于奈奎斯特率的测量次数完成信号恢复。文中详细阐述了测量矩阵的稳定性条件，包括约束等距性和不相干性等核心概念。阅读全文 →吴恩达：Agentic AI 课程资料 2025-10-17 ▼课程概览与学习入口阅读全文 →压缩感知-讲义 2007-07-01 ▼本文是关于压缩感知理论的讲座笔记，介绍了一种突破传统奈奎斯特采样定理限制的信号采集方法。通过利用信号的稀疏性或可压缩性，采用线性测量矩阵与优化重构算法，以远低于奈奎斯特率的测量次数完成信号恢复。文中详细阐述了测量矩阵的稳定性条件，包括约束等距性和不相干性等核心概念。阅读全文 →
