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
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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。
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反向传播的目标是计算损失函数相对于每个参数的偏导数，以便使用优化算法（如梯度下降）来更新参数。
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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。
根据问题的类型，输出层可以直接输出这些值（回归问题），或者通过激活函数（如softmax）转换为概率分布（分类问题）。