网络的权重和偏置如下（这些值是随机初始化的，实际情况中会使用随机初始化）：
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在神经网络中，损失函数通常是一个复合函数，由多个层的输出和激活函数组合而成。链式法则允许我们将这个复杂的复合函数的梯度计算分解为一系列简单的局部梯度计算，从而简化了梯度计算的过程。
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
With this situation, the consumer remains to be managing an older upstream Variation on the software with backport packages applied. This does not present the full security features and benefits of working the newest version in the program. Customers need to double-Test to determine the specific application update number to be sure They may be updating to the most recent Edition.
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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Even so, in select situations, it may be necessary to keep a legacy application If your more recent Edition of the appliance has steadiness concerns that will impact mission-vital operations.
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Backports might be a successful way to address security flaws and vulnerabilities in more mature versions of software package. Having said that, Every backport introduces a good number of complexity in the technique architecture and might be high priced to maintain.
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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。
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。