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2 changes: 1 addition & 1 deletion docs/content/ch01.md
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## 1.1 什么是语言模型
这里我们最开始从统计或者说统计学习的角度切入来讲这件事情，我们期望实现的是基于给定的文本信息输入，给出对应的新的文本/符号输出（可以是文本翻译、文本分类、文本扩写）。
要实现这样一个任务要解决两个问题：
1）输入序列问题：由于这里输入的是文本信号，而计算机能进入神经网络处理和计算的是数值，所以我们需要讲字符通过一定方式转化为数值。
1）输入序列问题：由于这里输入的是文本信号，而计算机能进入神经网络处理和计算的是数值，所以我们需要将字符通过一定方式转化为数值。
2）输出序列问题：由于所需要输出的部分也是文本，而神经网络的输出是数值类型的（分类问题：二分类问题对应01输出，多分类对应多个01输出；回归问题：对应数值类型输出），所以需要建立神经网络的数值类型输出和最终字符输出的映射关系。

针对于第一个问题，其处理方式其实有很多种，比如最简单的，我们可以将输入序列进行编码，从而把字符转化为数值。
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