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Fix wording in autograd section #1413

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Fix wording in autograd section #1413

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2 changes: 1 addition & 1 deletion chapter_preliminaries/autograd.md
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Expand Up @@ -196,7 +196,7 @@ x.grad
对于高阶和高维的`y`和`x`，求导的结果可以是一个高阶张量。

然而，虽然这些更奇特的对象确实出现在高级机器学习中（包括[**深度学习中**]），
但当调用向量的反向计算时，我们通常会试图计算一批训练样本中每个组成部分的损失函数的导数。
但当调用向量的反向传播时，我们通常会试图计算一批训练样本中每个组成部分的损失函数的导数。
这里(**，我们的目的不是计算微分矩阵，而是单独计算批量中每个样本的偏导数之和。**)

```{.python .input}
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