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）：
反向传播算法利用链式法则，通过从输出层向输入层逐层计算误差梯度，高效求解神经网络参数的偏导数，以实现网络参数的优化和损失函数的最小化。
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CrowdStrike’s knowledge science crew confronted this specific dilemma. This short article explores the workforce’s conclusion-earning approach along with the measures the team took to update somewhere around 200K lines of Python into a contemporary framework.
通过链式法则，我们可以从输出层开始，逐层向前计算每个参数的梯度，这种逐层计算的方式避免了重复计算，提高了梯度计算的效率。
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Backporting has a lot of rewards, nevertheless it really is BackPR on no account a simple repair to complex stability problems. Further more, relying on a backport from the lengthy-term may possibly introduce other stability threats, the chance of which can outweigh that of the initial situation.
偏导数是指在多元函数中，对其中一个变量求导，而将其余变量视为常数的导数。
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，实际的计算一下，体会一下这个过程之后再来推导公式，这样就会觉得很容易了。
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）。