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Books/动手学深度学习/基础概念.md
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@@ -12,4 +12,3 @@ $L(\mathbf{w}, b) = \frac1n\sum_{i=1}^{n} l^i(\mathbf{x}, b)$
其中$\eta$代表学习率
# 激活函数
在MLP中，由于多个线性层叠加，最终的结果和单层感知机并无区别，加入激活函数后，可以破坏这种线性叠加。
# 计算图