├── GPTPrompt-AndrewNg ├── README.md └── 吴恩达提示词教程.md ├── MISC_Workflow ├── Cursor & Copilot.md ├── Markdown基础语法.md ├── Overleaf & LaTeX.md ├── README.md ├── 文献阅读利器.md └── 知识管理方法论.md ├── ML_Handwriting_Code ├── 0 机器学习手写代码.md ├── 1 Linear Regression.md ├── 2 Logistic Regression.md ├── 3 Decision Tree.md ├── 4 Native Bayes.md ├── 5 K-means.md └── README.md ├── MachineLearningSpecialization-AndrewNg ├── 0 吴恩达机器学习ML.md ├── 1 概述Summarizazion.md ├── 11 决策树与随机森林.md ├── 13 聚类Clustering.md ├── 14 降维Dimensionality Reduction.md ├── 2 有or无监督学习.md ├── 3 单变量线性回归Univariate Linear Regression和代价(成本)函数Cost Function.md ├── 4 梯度下降Gradient Descent.md ├── 5 线性代数回顾Linear Algebra.md ├── 6 多变量线性回归Linear Regression with Multiple Variables.md ├── 7 逻辑回归Logistic Regression.md ├── 8 正则化Regularization.md ├── 9 神经网络-表述Neural Networks-Representation.md └── README.md ├── Python_Basic_Manual ├── 0 Python基础.md ├── 1~2 变量和简单数据类型.md ├── 21 高阶函数.md ├── 3~4 列表与元组.md ├── 5 条件与循环.md ├── 6 字典与集合.md ├── 7 用户输入与循环.md ├── 8 函数.md ├── 9 类.md ├── README.md └── X1 错误,异常捕获和处理.md ├── Python_Numerical_Analysis ├── 0 Python数据分析.md ├── 1 Python数据分析之Jupyter Notebook与Python.md ├── 2 Python数据分析之Numpy.md ├── 3 Python数据分析之Pandas.md ├── 4 Python数据分析之Matplotlib.md ├── 5 Python特征工程.md └── README.md ├── README.md ├── Sklearn_Manual ├── 0 引言.md ├── 1 机器学习简介.md ├── 2 Sklearn 数据.md ├── README.md └── 计算学习理论.md ├── Whiteboard_Derivation ├── 0 白板推导.md ├── 1 绪论.md ├── 2 数学基础.md ├── 3 线性回归.md ├── 4 逻辑回归.md ├── 5 决策树.md ├── 6 朴素贝叶斯.md ├── 7 K-Means.md └── README.md └── l /GPTPrompt-AndrewNg/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /GPTPrompt-AndrewNg/吴恩达提示词教程.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MITSUKI-CYBERPUNK/Machine-Learning-Notes/HEAD/GPTPrompt-AndrewNg/吴恩达提示词教程.md 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