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2 changes: 1 addition & 1 deletion docs/chapter2/第二章 Transformer架构.md
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随着 NLP 从统计机器学习向深度学习迈进，作为 NLP 核心问题的文本表示方法也逐渐从统计学习向深度学习迈进。正如我们在第一章所介绍的，文本表示从最初的通过统计学习模型进行计算的向量空间模型、语言模型，通过 Word2Vec 的单层神经网络进入到通过神经网络学习文本表示的时代。但是，从 计算机视觉（Computer Vision，CV）为起源发展起来的神经网络，其核心架构有三种：

- 全连接神经网络（Feedforward Neural Network，FNN），即每一层的神经元都和上下两层的每一个神经元完全连接，如图2.1所示:
- 全连接神经网络（Fully Connected Neural Network, FCNN），即每一层的神经元都和上下两层的每一个神经元完全连接，如图2.1所示:

<div align="center">
<img src="https://raw.githubusercontent.com/datawhalechina/happy-llm/main/docs/images/2-figures/1-0.png" alt="图片描述" width="90%"/>
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