深度学习(Deep Learning)：“Deep”的作用【“Modularization”、“End-to-End Learning”】

怼烎@ 2023-10-05 16:07 94阅读 0赞

## 一、Modularization ##

### 1、Universality Theorem ###

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### 2、“Thin + Tall” v.s. “Fat + Short” ###

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### 3、“Fat + Short” 模式下 ###

由于有些分类下的数据比较少， “Fat + Short” 模型对数据少的样本的分类训练的不好

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### 4、“Thin + Tall” 模式下 ###

将特征分解，再组合。  
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### 5、多层Layer的作用 ###

*  中间hidden layer 的作用类似于将特征数据值从“低维” → t r a n s f o r m e r \\xrightarrow\{transformer\} transformer“高维”，充分挖掘“特征数据值”在各个维度中的特性。

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带有非线性激活函数的Layer层数越多，则模型能模拟的函数的复杂度越高  
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## 二、End-to-End Learning ##

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### 1、End-to-End Learning：语音识别 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70 2]  
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### 2、End-to-End Learning：图像识别 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70 3]  
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参考资料：  
[A visual proof that neural nets can compute any function][]  
[Deep Learning: Theoretical Motivations (Yoshua Bengio)][Deep Learning_ Theoretical Motivations _Yoshua Bengio]  
[Why Deep Learning Works: Perspectives from Theoretical Chemistry][Why Deep Learning Works_ Perspectives from Theoretical Chemistry]

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[A visual proof that neural nets can compute any function]: http://neuralnetworksanddeeplearning.com/chap4.html
[Deep Learning_ Theoretical Motivations _Yoshua Bengio]: http://videolectures.net/deeplearning2015_bengio_theoretical_motivations/
[Why Deep Learning Works_ Perspectives from Theoretical Chemistry]: https://www.youtube.com/watch?v=kIbKHIPbxiU