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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A backport is most commonly utilized to address security flaws in legacy software or older versions on the program that are still supported via the developer.
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 standard approach to deal with a acknowledged bug within the IT atmosphere. Concurrently, relying on a legacy codebase introduces other perhaps significant stability implications for companies. Depending on outdated or legacy code could bring about introducing weaknesses or vulnerabilities within your environment.
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Decide what patches, updates or modifications can be obtained to handle this challenge in later on versions of precisely the same application.
的基础了，但是很多人在学的时候总是会遇到一些问题，或者看到大篇的公式觉得好像很难就退缩了，其实不难，就是一个链式求导法则反复用。如果不想看公式，可以直接把数值带进去，实际的计算一
的原理及实现过程进行说明，通俗易懂，适合新手学习，附源码及实验数据集。
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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
根据计算得到的梯度信息，使用梯度下降或其他优化算法来更新网络中的权重和偏置参数，以最小化损失函数。
参数偏导数：在计算了输出层和隐藏层的偏导数之后，我们需要进一步计算损失函数相对于网络参数的偏导数，即权重和偏置的偏导数。
利用计算得到的误差梯度，可以进一步计算每个权重和偏置参数对于损失函数的梯度。