CNN Why-conv #581
CNN Why-conv #581
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| 例如，让我们回到我们区分猫和狗的运行例子。假设我们做一个彻底的工作，在数据收集，收集一百万像素照片的注释数据集。这意味着网络的每个输入都有一百万个维度。即使大幅缩小到千个隐藏尺寸，也需要一个以 $10^6 \times 10^3 = 10^9$ 参数为特征的完全连接层。除非我们拥有大量的 GPU、分布式优化的天赋以及极大的耐心，否则学习这个网络的参数可能会被证明是不可行的。 | ||
| 例如，在之前区分猫和狗的例子中，假设我们收集了一个照片数据集，每张照片具有百万级像素，这意味着MLP的每个输入都有一百万个维度。然而即使MLP只有 $1000$ 个隐藏层，这个神经网络将有 $10^6 \times 10^3 = 10^9$ 个参数。除非我们有大量的GPU，分布式优化训练的经验以及超乎常人的耐心，否则很难学习这个网络。 |
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- 第一句可以拆成两个短句。
- “然而即使MLP只有
$1000$ 个隐藏层”意思不对。应该是“即使将隐藏层维度降低到1000” - “然而……这个神经网络也将有”
- “除非我们有大量的GPU，分布式优化训练的经验以及超乎常人的耐心，否则很难学习这个网络。”中间表示并列的逗号改成顿号
- “否则很难学习这个网络。”听起来像是我们在学啥
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LOL, any suggestion to translate the last sentence?
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想要使这个模型学习良好是很难的事情，需要有大量的GPU、分布式优化训练的经验和超乎常人的耐心。
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How about "想要训练这个模型很难，需要有大量的GPU、分布式优化训练的经验和超乎常人的耐心。"?
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MLP这种不要放英文缩写，用中文全称：多层感知机
Fixed!
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