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。
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As reviewed inside our Python blog site submit, each backport can make lots of undesirable Unwanted effects in the IT surroundings.
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反向传播算法基于微积分中的链式法则，通过逐层计算梯度来求解神经网络中参数的偏导数。
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Backporting has lots of strengths, although it is on no account a simple correct to intricate protection challenges. Further, depending on a backport inside the lengthy-term may introduce other stability threats, the risk of which can outweigh that of the original difficulty.
Backports is often a powerful way to address protection flaws and vulnerabilities in more mature versions of computer software. Nevertheless, Each individual backport introduces a fair number of complexity throughout the procedure architecture and might be high priced to keep up.
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。
在神经网络中，偏导数用于量化损失函数相对于模型参数（如权重和偏置）的变化率。
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。