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Correct typos #44

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Correct typos #44

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2 changes: 1 addition & 1 deletion Deep Learning/10. Neural Network/README.md
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Expand Up @@ -185,7 +185,7 @@
2. 可微性： 当优化方法是基于梯度的时候，就体现了该性质；
3. 单调性： 当激活函数是单调的时候，单层网络能够保证是凸函数；
4. ![](http://wx1.sinaimg.cn/mw690/00630Defgy1g5nmaak1f3j302x0120si.jpg)： 当激活函数满足这个性质的时候，如果参数的初始化是随机的较小值，那么神经网络的训练将会很高效；如果不满足这个性质，那么就需要详细地去设置初始值；
5. 输出值的范围： 当激活函数输出值是有限的时候，基于梯度的优化方法会更加稳定，因为特征的表示受有限权值的影响更显著；当激活函数的输出是无限的时候，模型的训练会更加高效，不过在这种情况小，一般需要更小的 Learning Rate。
5. 输出值的范围： 当激活函数输出值是有限的时候，基于梯度的优化方法会更加稳定，因为特征的表示受有限权值的影响更显著；当激活函数的输出是无限的时候，模型的训练会更加高效，不过在这种情况下，一般需要更小的 Learning Rate。

### 3.4 正向传播

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