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深度学习与大模型学习路径（2026） 面向中文学习者的 AI 学习路线图：从机器学习、深度学习基础，到大语言模型、RAG、Agent 和多模态工程实践。 本仓库的定位不是资料堆砌，而是帮你回答三个问题： 现在应该先学什么。 每个主题学到什么程度算够用。 如何从概念走到可运行的 AI 应用。 学习理念 AI 工具普及后，学习方式应该从“先补完所有理论”变成“先跑通最小闭环，再按问题补理论”。 本仓库采用四个原则： 先实践后理论：先让模型、数据和代码跑起来。 只深挖关键概念：向量化、梯度、过拟合、Transformer、检索、工具调用、评估。 按需回溯：遇到诊断、调优、评估问题时再回到数学和经典算法。 关注工程闭环：能部署、能评估、能定位错误，比只看懂概念更重要。 该从哪里开始 30 天新人路线 适合：从零开始，希望快速进入 AI/ML 领域。 建议顺序： Python 基础 NumPy Pandas 机器学习绪论 线性回归 逻辑回归 深度学习基础 PyTorch 基础 LLM 入门
| Stars | 2,566 |
| Forks | 396 |
| Language | Jupyter Notebook |
| Category | AI Tool |
| Quality Score | 44.7899935759458/100 |
| Open Issues | 7 |
| Last Updated | 2026-06-27 |
| Created | 2019-01-31 |
| Est. Tokens | ~17k |
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deep-learning-llm-agent-notes is 机器学习、深度学习的学习路径及知识总结. It is categorized as a AI Tool with 2.6k GitHub stars.
deep-learning-llm-agent-notes is primarily written in Jupyter Notebook.
You can find installation instructions and usage details in the deep-learning-llm-agent-notes GitHub repository at github.com/loveunk/deep-learning-llm-agent-notes. The project has 2.6k stars and 396 forks, indicating an active community.
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