diff --git a/.obsidian/app.json b/.obsidian/app.json index 06f45c7..cf47506 100644 --- a/.obsidian/app.json +++ b/.obsidian/app.json @@ -1,7 +1,7 @@ { "promptDelete": false, "newLinkFormat": "relative", - "attachmentFolderPath": "./assets/引言", + "attachmentFolderPath": "./assets/基础概念", "showUnsupportedFiles": false, "newFileLocation": "current", "useMarkdownLinks": true, diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index 3821b78..7f27a54 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -13,7 +13,7 @@ "state": { "type": "markdown", "state": { - "file": "Books/动手学深度学习/引言.md", + "file": "Books/动手学深度学习/基础概念.md", "mode": "source", "source": false } @@ -77,7 +77,8 @@ } ], "direction": "horizontal", - "width": 315.5 + "width": 315.5, + "collapsed": true }, "right": { "id": "c501495747cfa761", @@ -93,7 +94,7 @@ "state": { "type": "backlink", "state": { - "file": "Books/动手学深度学习/引言.md", + "file": "Books/动手学深度学习/基础概念.md", "collapseAll": false, "extraContext": false, "sortOrder": "alphabetical", @@ -110,7 +111,7 @@ "state": { "type": "outgoing-link", "state": { - "file": "Books/动手学深度学习/引言.md", + "file": "Books/动手学深度学习/基础概念.md", "linksCollapsed": false, "unlinkedCollapsed": true } @@ -133,7 +134,7 @@ "state": { "type": "outline", "state": { - "file": "Books/动手学深度学习/引言.md" + "file": "Books/动手学深度学习/基础概念.md" } } }, @@ -166,9 +167,10 @@ }, "active": "82a60b2f86acd8d6", "lastOpenFiles": [ + "Books/动手学深度学习/引言.md", + "Books/动手学深度学习/基础概念.md", "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", "Books/从零开始深入学习Spring/IoC.md", "Books/Vim实用技巧/一、入门导读.md", "Books/从零开始深入学习Spring", diff --git a/Books/动手学深度学习/基础概念.md b/Books/动手学深度学习/基础概念.md new file mode 100644 index 0000000..a845d86 --- /dev/null +++ b/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 diff --git a/Books/动手学深度学习/引言.md b/Books/动手学深度学习/引言.md index d330b59..8b13789 100644 --- a/Books/动手学深度学习/引言.md +++ b/Books/动手学深度学习/引言.md @@ -1,2 +1 @@ -# 关键部分 -## 数据 +