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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）：
This method is as clear-cut as updating quite a few lines of code; it could also include a major overhaul that is certainly unfold throughout a number of documents of your code.
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。
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Just as an upstream software program software impacts all downstream apps, so much too does a backport placed on the core application. This is also correct Should the backport is used throughout the kernel.
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。
Backporting necessitates usage of the application’s source code. As a result, the backport might be created and furnished by the Main growth crew for closed-supply software program.
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Backports is often a successful way to deal with protection flaws and vulnerabilities in older variations of software. Nevertheless, Every backport introduces a good number of complexity in the program architecture and can be highly-priced to keep up.
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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。