├── 1.新手关卡 ├── README.md └── 提问的智慧.md ├── 2.理论篇 ├── 1.基础知识 │ └── README.md ├── 2.机器学习 │ └── README.md ├── 3.计算机视觉 │ └── README.md ├── 4.自然语言处理 │ └── README.md ├── 5.强化学习 │ └── README.md └── 6.启发式算法 │ └── README.md ├── 3.编程篇 ├── 1.基础知识 │ └── README.md ├── 2.机器学习 │ └── README.md ├── 3.计算机视觉 │ └── README.md ├── 4.自然语言处理 │ └── README.md ├── 5.强化学习 │ └── README.md ├── 6.启发式算法 │ └── README.md ├── 7.数据分析 │ └── README.md └── 补充资料 │ └── 线性代数与Python.md ├── 4.应用篇 ├── 1.计算机视觉 │ └── README.md └── 2.自然语言处理 │ └── README.md ├── 4.项目篇 ├── 1.基础知识 │ └── README.md ├── 2.机器学习 │ └── README.md ├── 3.计算机视觉 │ └── README.md ├── 4.自然语言处理 │ └── README.md ├── 5.强化学习 │ └── README.md ├── 6.启发式算法 │ └── README.md └── 7.数据分析 │ └── README.md ├── 5.拓展 ├── .NET │ └── README.md ├── 前端开发 │ └── README.md ├── 后端开发 │ ├── README.md │ └── 各种语言的后端技术栈.md ├── 嵌入式系统开发 │ └── README.md ├── 数据库 │ └── README.md ├── 硬件开发 │ └── README.md ├── 系统开发 │ └── README.md ├── 网络工程 │ └── README.md └── 运维 │ └── README.md ├── README.md └── 从python开始的ai学习 ├── python基础与数据科学入门 ├── .ipynb_checkpoints │ └── 有益的探索-checkpoint.ipynb ├── 1. 有趣的探索 │ ├── .ipynb_checkpoints │ │ └── 有益的探索-checkpoint.ipynb │ ├── 图片等杂项 │ │ ├── .gitkeep │ │ ├── 关系图.png │ │ ├── 字典.png │ │ ├── 字符串.png │ │ ├── 实现原理1.png │ │ ├── 实现原理2.png │ │ ├── 实现原理3.png │ │ ├── 实现原理4.png │ │ ├── 引用数组概念图.png │ │ ├── 思维导图.png │ │ ├── 数据类型.png │ │ ├── 更简洁.png │ │ ├── 测试文件.txt │ │ ├── 神器.png │ │ └── 问题原理1.png │ └── 有益的探索（看这个）.ipynb ├── 2. Python标准库 │ ├── .ipynb_checkpoints │ │ ├── Python标准库-checkpoint.ipynb │ │ └── Untitled-checkpoint.ipynb │ ├── Python标准库（看这个）.ipynb │ └── 图片等杂项 │ │ ├── collections.png │ │ ├── itertools.png │ │ ├── random.png │ │ ├── time.png │ │ └── 思维导图.png ├── 3. numpy库 │ ├── .ipynb_checkpoints │ │ └── numpy库-checkpoint.ipynb │ ├── numpy库（看这个）.ipynb │ └── 图片等杂项 │ │ ├── axis1.png │ │ ├── axis2.png │ │ ├── why.png │ │ ├── 函数.png │ │ ├── 创建.png │ │ ├── 广播规则.png │ │ ├── 思维导图.png │ │ ├── 性质.png │ │ └── 运算.png ├── 4. Pandas库 │ ├── .ipynb_checkpoints │ │ ├── Pandas库-checkpoint.ipynb │ │ └── Untitled-checkpoint.ipynb │ ├── Pandas库（看这个）.ipynb │ └── 图片等杂项 │ │ ├── dt1.png │ │ ├── dt2.png │ │ └── dt3.png ├── 5. Matplotlib库 │ ├── .ipynb_checkpoints │ │ ├── Matplotlib库（看这个）-checkpoint.ipynb │ │ └── Untitled-checkpoint.ipynb │ ├── Matplotlib库（看这个）.ipynb │ └── pcs │ │ ├── dt1.png │ │ ├── dt2.png │ │ ├── housing.csv │ │ └── my_figure.png └── 6. Sklearn库 │ ├── .ipynb_checkpoints │ ├── Sklearn库-checkpoint.ipynb │ └── Untitled-checkpoint.ipynb │ ├── Sklearn库（看这个）.ipynb │ └── pcs │ ├── dt1.png │ ├── iris_predict.csv │ ├── k.png │ └── svc.png ├── 机器学习算法总结 ├── .idea │ ├── .gitignore │ ├── inspectionProfiles │ │ └── profiles_settings.xml │ ├── misc.xml │ ├── modules.xml │ ├── workspace.xml │ └── 机器学习算法总结.iml ├── Lasso回归 │ ├── Lasso回归.md │ └── Lasso回归.py ├── boston_housing.csv ├── 一. 监督学习 │ ├── 1. 线性回归(Linear Regression) │ │ ├── Linear Regression.py │ │ └── 线性回归.md │ ├── 2. 多项式回归(Polynomial Regression) │ │ ├── pcs │ │ │ └── image.png │ │ ├── 多项式回归.md │ │ └── 多项式回归.py │ ├── 3. 岭回归(Ridge Regression) │ │ ├── 岭回归.md │ │ └── 岭回归.py │ ├── 4. Lasso回归(Lasso Regression) │ │ ├── Lasso回归.md │ │ └── Lasso回归.py │ ├── 5. 弹性网络回归(Elastic Net Regression) │ │ ├── 弹性网络回归.md │ │ └── 弹性网络回归.py │ ├── 6. 逻辑回归(Logistic Regression) │ │ ├── 逻辑回归.md │ │ └── 逻辑回归.py │ ├── 7. 决策树回归(Decision Tree Regression) │ │ ├── Decision Tree Regression.md │ │ ├── Decision Tree Regression.py │ │ └── decision_tree.png │ ├── 8. 随机森林回归(Random Forest Regression) │ │ ├── Random Forest Regression.md │ │ └── Random Forest Regression.py │ ├── 9. 支持向量机(SVM) │ │ ├── SVM.md │ │ └── SVM.py │ └── boston_housing.csv ├── 多项式回归 │ ├── pcs │ │ └── image.png │ ├── 多项式回归.md │ └── 多项式回归.py ├── 岭回归 │ ├── 岭回归.md │ └── 岭回归.py └── 线性回归 │ ├── Linear Regression.py │ └── 线性回归.md └── 深度学习 pytorch ├── .idea ├── .gitignore ├── .name ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── 深度学习pytorch.iml ├── 1.张量简介与创建 ├── .idea │ ├── .name │ ├── inspectionProfiles │ │ └── profiles_settings.xml │ ├── misc.xml │ ├── modules.xml │ ├── vcs.xml │ ├── workspace.xml │ └── 张量简介与创建.iml ├── pcs │ ├── 1.png │ ├── 1_1.png │ ├── 1_2.png │ ├── 1_3.png │ ├── 1_4.png │ ├── 1_5.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ └── 5.png ├── tensor.py └── 张量简介与创建.md ├── 10. 模型容器与AlexNet构建 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ └── 8.png └── 模型容器与AlexNet构建.md ├── 11. nn网络层-卷积层 ├── nn网络层-卷积层.md └── pcs │ ├── 1.png │ ├── 10.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png ├── 12. nn网络层-池化-线性-激活函数层 ├── nn网络层-池化-线性-激活函数层.md └── pcs │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png ├── 13. 权值初始化 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ └── 6.png └── 权值初始化.md ├── 14. 损失函数（1） ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ └── 6.png └── 损失函数（1）.md ├── 15. 损失函数（2） ├── pcs │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── 损失函数（2）.md ├── 16. 优化器optimizer（1）概念 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ └── 5.png └── 优化器optimizer的概念.md ├── 17. 优化器optimizer（2） ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ └── 7.png └── 优化器（2）.md ├── 18. 学习率调整策略 ├── pcs │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── 学习率调整策略.md ├── 19. Tensorboard的简介安装和使用 ├── Tensorboard的简介安装与使用.md └── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png ├── 2.张量操作与线性回归 ├── Linear Regression.py ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 3.png │ └── 4.png ├── tensor process.py └── 张量操作与线性回归.md ├── 20. hook函数与CAM可视化 ├── hook函数与CAM可视化.md └── pcs │ ├── 1.png │ ├── 10.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png ├── 21. 正则化之weight_decay ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ └── 6.png └── 正则化之weight_decy.md ├── 22. 正则化之Dropout ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ └── 4.png └── 正则化之Dropout.md ├── 23. Batch Normalization ├── Batch Normalization.md └── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ └── 6.png ├── 24. 更多Normalization方法 ├── pcs │ ├── 1.png │ ├── 10.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── 更多Normalization方法.md ├── 25. 模型加载与保存 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ └── 4.png └── 模型保存与加载.md ├── 26. 模型finetune ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ └── 5.png └── 模型finetune.md ├── 27. GPU的使用 ├── GPU的使用.md └── pcs │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png ├── 28. PyTorch常见报错 ├── PyTorch的常见报错.md └── pcs │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png ├── 3.计算图与动态图机制 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 31.png │ ├── 32.png │ └── 33.png ├── 计算图与动态图机制.md └── 计算图与动态图机制.py ├── 4.autograd与逻辑回归 ├── Logistic Regression.py ├── autograd与逻辑回归.md ├── autograd与逻辑回归.py └── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 41.png │ ├── 42.png │ ├── 43.png │ ├── 44.png │ ├── 45.png │ ├── 46.png │ ├── 5.png │ ├── 6.png │ ├── Logistic Regression 0.png │ ├── Logistic Regression 100.png │ ├── Logistic Regression 120.png │ ├── Logistic Regression 20.png │ ├── Logistic Regression 40.png │ ├── Logistic Regression 60.png │ └── Logistic Regression 80.png ├── 5.数据读取机制： Dataloader 与 Dataset ├── .ipynb_checkpoints │ └── Untitled-checkpoint.ipynb ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 52.png │ ├── 53.png │ ├── 54.png │ ├── 55.png │ ├── 56.png │ └── 6.png └── 数据读取机制：Dataloader与Dataset.md ├── 6.数据预处理：transforms模块机制 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 61.png │ ├── 62.png │ └── 63.png └── 数据预处理：transforms模块机制.md ├── 7. transforms图像增强与多种数据预处理方法 ├── pcs │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── transforms图像增强与多种数据预处理方法.md ├── 8. transform图像增强与自定义数据预处理方法 ├── pcs │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── transform图像增强与自定义数据预处理方法.md └── 9. 模型创建步骤与nn.Module ├── pcs ├── 1.png ├── 2.png ├── 3.png ├── 4.png ├── 5.png ├── 6.png └── 7.png └── 模型创建步骤与nn.Module.md /1.新手关卡/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Discrete-Mathematics/ai-self-learning/HEAD/1.新手关卡/README.md -------------------------------------------------------------------------------- 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