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KevinZonda · May 23, 2024 · b3e886c · b3e886c
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diff --git a/2-Supervised/2.1-LinearRegression.md → 2-Linear/2.1-LinearRegression.md b/2-Supervised/2.1-LinearRegression.md → 2-Linear/2.1-LinearRegression.md
diff --git a/2-Supervised/2.2-GD.md → 2-Linear/2.2-GD.md b/2-Supervised/2.2-GD.md → 2-Linear/2.2-GD.md
diff --git a/2-Supervised/2.3-LinearRegressionGD.md → 2-Linear/2.3-LinearRegressionGD.md b/2-Supervised/2.3-LinearRegressionGD.md → 2-Linear/2.3-LinearRegressionGD.md
diff --git a/2-Supervised/2.4-LogisticRegression.md → 2-Linear/2.4-LogisticRegression.md b/2-Supervised/2.4-LogisticRegression.md → 2-Linear/2.4-LogisticRegression.md
diff --git a/2-Supervised/2.5-NonLinearTransformation.md → 2-Linear/2.5-NonLinearTransformation.md b/2-Supervised/2.5-NonLinearTransformation.md → 2-Linear/2.5-NonLinearTransformation.md
diff --git a/2-Supervised/2.6-SVM.md → 2-Linear/2.6-SVM.md b/2-Supervised/2.6-SVM.md → 2-Linear/2.6-SVM.md
diff --git a/2-Supervised/2.7-KernelTrick.md → 2-Linear/2.7-KernelTrick.md b/2-Supervised/2.7-KernelTrick.md → 2-Linear/2.7-KernelTrick.md
diff --git a/2-Supervised/2.8-SVR.md → 2-Linear/2.8-SVR.md b/2-Supervised/2.8-SVR.md → 2-Linear/2.8-SVR.md
diff --git a/2-Linear/README.md b/2-Linear/README.md
@@ -0,0 +1,3 @@
+# 2. 线性模型
+
+我们在这一节将主要关注传统机器学习中的线性模型，包括线性回归、逻辑回归等。这些算法是机器学习中最基础的算法，也是很多其他算法的基础。
diff --git a/2-Supervised/img/gd-left.png → 2-Linear/img/gd-left.png b/2-Supervised/img/gd-left.png → 2-Linear/img/gd-left.png
diff --git a/2-Supervised/img/gd-right.png → 2-Linear/img/gd-right.png b/2-Supervised/img/gd-right.png → 2-Linear/img/gd-right.png
diff --git a/2-Supervised/img/gd-taylor.png → 2-Linear/img/gd-taylor.png b/2-Supervised/img/gd-taylor.png → 2-Linear/img/gd-taylor.png
diff --git a/2-Supervised/img/linear-ression.png → 2-Linear/img/linear-ression.png b/2-Supervised/img/linear-ression.png → 2-Linear/img/linear-ression.png
diff --git a/2-Supervised/README.md b/2-Supervised/README.md
diff --git a/SUMMARY.md b/SUMMARY.md
@@ -10,15 +10,15 @@
   - [概率论 [WIP]](./1-Basic/1.5-ProbabilityTheory.md)
   - [机器学习问题定义](./1-Basic/1.6-MLProblem.md)
   - [不同机器学习种类](./1-Basic/1.7-Kinds.md)
-- [监督学习](./2-Supervised/README.md)
-  - [线性回归](./2-Supervised/2.1-LinearRegression.md)
-  - [梯度下降 GD](./2-Supervised/2.2-GD.md)
-  - [线性回归与梯度下降](./2-Supervised/2.3-LinearRegressionGD.md)
-  - [逻辑回归](./2-Supervised/2.4-LogisticRegression.md)
-  - [非线性变换](./2-Supervised/2.5-NonLinearTransformation.md)
-  - [支持向量机 SVM [WIP]](./2-Supervised/2.6-SVM.md)
-  - [核技巧 Kernel Trick [WIP]](./2-Supervised/2.7-KernelTrick.md)
-  - [支持向回归 SVR [WIP]](./2-Supervised/2.8-SVR.md)
+- [监督学习](./2-Linear/README.md)
+  - [线性回归](./2-Linear/2.1-LinearRegression.md)
+  - [梯度下降 GD](./2-Linear/2.2-GD.md)
+  - [线性回归与梯度下降](./2-Linear/2.3-LinearRegressionGD.md)
+  - [逻辑回归](./2-Linear/2.4-LogisticRegression.md)
+  - [非线性变换](./2-Linear/2.5-NonLinearTransformation.md)
+  - [支持向量机 SVM [WIP]](./2-Linear/2.6-SVM.md)
+  - [核技巧 Kernel Trick [WIP]](./2-Linear/2.7-KernelTrick.md)
+  - [支持向回归 SVR [WIP]](./2-Linear/2.8-SVR.md)
 - [机器学习实践](./MLPractice/README.md)
   - [梯度下降的变种](./MLPractice/GDVariants.md)
   - [过拟合和欠拟合](./MLPractice/OverfitUnderfit.md)
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		# 2. 线性模型

		我们在这一节将主要关注传统机器学习中的线性模型，包括线性回归、逻辑回归等。这些算法是机器学习中最基础的算法，也是很多其他算法的基础。