技术取得了令人瞩目的成就,在图像识别、自然语言处理、语音识别等领域取得了突破性的进展。这些成就离不开大模型的快速发展。大模型是指参数量庞大的
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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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。
was the final official release of Python 2. So as to remain present with protection patches and go on taking pleasure in every one of the new developments Python provides, organizations required to enhance to Python 3 or get started freezing necessities and decide to legacy prolonged-phrase support.
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CrowdStrike’s knowledge science staff confronted this actual Predicament. This information explores the staff’s selection-producing process in addition to the methods the group took to update close to 200K lines of Python into a modern 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。
来计算梯度,我们需要调整权重矩阵的权重。我们网络的神经元(节点)的权重是通过计算损失函数的梯度来调整的。为此
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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。
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