Merge remote-tracking branch 'origin/main'

.obsidian/app.json

@@ -1,7 +1,7 @@
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+  "attachmentFolderPath": "./assets/基础概念",
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.obsidian/workspace.json

@@ -13,7 +13,7 @@
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+                "file": "Books/动手学深度学习/基础概念.md",
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@@ -77,7 +77,8 @@
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@@ -93,7 +94,7 @@
             "state": {
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@@ -110,7 +111,7 @@
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-                "file": "Books/动手学深度学习/引言.md",
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@@ -133,7 +134,7 @@
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@@ -166,9 +167,10 @@
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     "Books/Java Guide/基础语法.md",
     "Books/Java并发编程/Java并发编程.md",
-    "Books/动手学深度学习/引言.md",
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Books/动手学深度学习/基础概念.md

@@ -0,0 +1,12 @@
+# 损失函数
+用来量化预测值与实际值之间的差距。
+一般我们会使用平方误差：
+$l^i(\mathbf{w}, b) = \frac{1}{2}( \hat{y}^i - y^i)$
+损失函数我们则采用平方误差的均值：
+$L(\mathbf{w}, b) = \frac1n\sum_{i=1}^{n} l^i(\mathbf{x}, b)$
+# 优化算法
+- 随机梯度下降算法(Stochastic Gradient Descent)
+  通过不断在损失函数递减方向上更新参数来降低误差。
+  梯度下降法主要计算损失函数关于模型参数的导数。但是每次计算时候遍历整个数据集，效率会很低。所以每次计算先抽取一个小批量$B$(由固定数量的样本组成)的梯度,然后我们将梯度乘以一个预先确定的正数$\eta$，并从当前采纳数的值中减掉。
+  $(\mathbf{w}, b) <- (\mathbf{w},b) - \frac{\eta}{|B|} \sum_{i\in{B}}\partial_{(\mathbf{w}, b)}l^i(\mathbf{w},b)$
+  其中$\eta$代表学习率

Books/动手学深度学习/引言.md

@@ -1,2 +1 @@
-# 关键部分
+
-## 数据