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vault backup: 2023-10-23 20:39:28

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Rain&Bus
2023-10-23 20:39:29 +08:00
parent 8af0c88db0
commit eac6ddd746
2 changed files with 2 additions and 2 deletions
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.obsidian/appearance.json vendored
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@@ -2,7 +2,7 @@
"accentColor": "",
"cssTheme": "Minimal",
"monospaceFontFamily": "Maple Mono SC NF",
"theme": "moonstone",
"theme": "obsidian",
"interfaceFontFamily": "霞鹜文楷",
"textFontFamily": "霞鹜文楷等宽",
"translucency": false

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Study/DL/吴恩达深度学习课程/Lesson 1.md
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@@ -18,7 +18,7 @@ b &= b - \eta \cdot db
反向传递：更新模型参数。
sigmoid函数：消除线性。
> 线性激活函数: $a = z$
> 如果我们使用线性激活函数，无论我们经过多少层网络迭代，都相当于是对输入进行线性变换，
> 如果我们使用线性激活函数，无论我们经过多少层网络迭代，都相当于是对输入进行线性变换。
损失函数：计算模型预测结果的精度，反向传播的目的就是使得。
## Vectorization