DeepLearning.ai深度学习课程笔记
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3.8 神经网络的梯度下降（Gradient descent for neural networks）
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3.7 激活函数的导数（Derivatives of activation functions ）
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3.9 （选修）直观理解反向传播（Backpropagation intuition ）
Last updated
5 years ago
d
Z
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2
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=
A
[
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−
Y
dZ^{[2]}=A^{[2]}-Y
d
Z
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=
A
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2
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−
Y
d
W
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=
1
m
d
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2
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A
[
1
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T
dW^{[2]}=\frac1mdZ^{[2]}A^{[1]T}
d
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=
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d
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A
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db^{[2]}=\frac1mnp.sum(dZ^{[2]},axis=1,keepdim=True)
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d
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∗
g
′
(
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dZ^{[1]}=W^{[2]T}dZ^{[2]}\ast g'(Z^{[1]})
d
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d
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d
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d
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dW^{[1]}=\frac1mdZ^{[1]}X^T
d
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d
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db^{[1]}=\frac1mnp.sum(dZ^{[1]},axis=1,keepdim=True)
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