链式法则不仅适用于简单的两层神经网络，还可以扩展到具有任意多层结构的深度神经网络。这使得我们能够训练和优化更加复杂的模型。
This can be performed as A part of an Formal patch or bug deal with. For open up-supply software package, like Linux, a backport could be supplied by a third party and then submitted into the software package growth group.
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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。
Backporting is a common system to handle a identified bug within the IT atmosphere. Concurrently, relying on a legacy codebase introduces other potentially major security implications for corporations. Relying on aged or legacy code could cause introducing weaknesses or vulnerabilities in your setting.
偏导数是多元函数中对单一变量求导的结果，它在神经网络反向传播中用于量化损失函数随参数变化的敏感度，从而指导参数优化。
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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Nevertheless, in pick out cases, it could be important to retain a legacy software In the event the more recent Model of the appliance has steadiness issues which could affect mission-crucial operations.
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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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一章中的网络是能够学习的，但我们只将线性网络用于线性可分的类。 当然，我们想写通用的人工
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）。