参数的过程中使用的一种求导法则。 具体来说，链式法则是将复合函数的导数表示为各个子函数导数的连乘积的一种方法。在
算法从输出层开始，根据损失函数计算输出层的误差，然后将误差信息反向传播到隐藏层，逐层计算每个神经元的误差梯度。
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隐藏层偏导数：使用链式法则，将输出层的偏导数向后传播到隐藏层。对于隐藏层中的每个神经元，计算其输出相对于下一层神经元输入的偏导数，并与下一层传回的偏导数相乘，累积得到该神经元对损失函数的总偏导数。
中，每个神经元都可以看作是一个函数，它接受若干输入，经过一些运算后产生一个输出。因此，整个
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Backporting has several positive aspects, even though it is not at all an easy resolve to complex safety difficulties. Additional, depending on a backport in the very long-time period may well introduce other safety threats, the potential risk of which can outweigh that of the initial issue.
过程中，我们需要计算每个神经元函数对误差的导数，从而确定每个参数对误差的贡献，并利用梯度下降等优化
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链式法则是微积分中的一个基本定理，用于计算复合函数的导数。如果一个函数是由多个函数复合而成，那么该复合函数的导数可以通过各个简单函数导数的乘积来计算。
Backporting can provide consumers a Untrue sense of safety In the event the enumeration course of action will not be entirely comprehended. One example is, users may read through media stories about upgrading their program to deal with safety problems. However, what they really do is install an current package deal from The seller and never the newest upstream Variation of the application.