July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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第 8章 深度学习
246
1
×
1 的滤波器的卷积层。这个 1
×
1 的卷积运算通过在通道方向上减小大小，
有助于减少参数和实现高速化处理（具体请参考原始论文
[23]
）。
8.2.4
ResNet
ResNet
[24]
是微软团队开发的网络。它的特征在于具有比以前的网络更
深的结构。
我们已经知道加深层对于提升性能很重要。但是，在深度学习中，过度
加深层的话，很多情况下学习将不能顺利进行，导致最终性能不佳。
ResNet
中，
为了解决这类问题，导入了“快捷结构”（也称为“捷径”或“小路”）。导入这
个快捷结构后，就可以随着层的加深而不断提高性能了（当然，层的加深也
是有限度的）。
如图8
-
12 所示，快捷结构横跨（跳过）了输入数据的卷积层，将输入
x
合
计到输出。
图 8
-
12 中，在连续 2 层的卷积层中，将输入x 跳着连接至2 层后的输出。
这里的重点是，通过快捷结构，原来的 2 层卷积层的输出
F(
x
)
变成了
F(
x
) +
x。
通过引入这种快捷结构，即使加深层，也能高效地学习。这是因为，通过快
捷结构，反向传播时信号可以无衰减地传递。
图8-11 GoogLeNet 的 Inception 结构（引用自文献 [23]）
8.2 深度学习的小历史
247
图8-12 ResNet 的构成要素（引用自文献[24]）：这里的 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.