# README

深度解析Transformer（大模型场景），提供图、代码等，力求每个人都能吃透它。

有疑惑的地方欢迎issue或邮件我，😀Enjoin！

[Attention Is All You Need 论文地址](https://arxiv.org/pdf/1706.03762)

[GPT-2 官方代码](https://www.tensorflow.org/tutorials/text/transformer?hl=zh-cn)

[动态图解 nanoGPT](https://bbycroft.net/llm)

### 学习章节：

<ul>
    <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/tree/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer'>人人都能看懂的Transformer</a>
    <ul>
               <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E4%B8%80%E7%AB%A0%E2%80%94%E2%80%94Transformer%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84.md'>第一章——Transformer网络架构</a></li>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E4%BA%8C%E7%AB%A0%E2%80%94%E2%80%94%E6%96%87%E5%AD%97%E5%90%91%E9%87%8F%E5%8C%96.md'>第二章——文字向量化</a>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E4%B8%89%E7%AB%A0%E2%80%94%E2%80%94%E4%BD%8D%E7%BD%AE%E7%BC%96%E7%A0%81.md'>第三章——位置编码</a>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E5%9B%9B%E7%AB%A0%E2%80%94%E2%80%94%E5%A4%9A%E5%A4%B4%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6%E2%80%94%E2%80%94QK%E7%9F%A9%E9%98%B5%E7%9B%B8%E4%B9%98.md'>第四章——多头注意力机制——QK矩阵相乘</a>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E4%BA%94%E7%AB%A0%E2%80%94%E2%80%94%E5%A4%9A%E5%A4%B4%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6%E2%80%94%E2%80%94%E5%85%A8%E6%B5%81%E7%A8%8B.md'>第五章——多头注意力机制——全流程</a>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E5%85%AD%E7%AB%A0%E2%80%94%E2%80%94%E6%95%B0%E5%80%BC%E7%BC%A9%E6%94%BE.md'>第六章——数值缩放</a>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E4%B8%83%E7%AB%A0%E2%80%94%E2%80%94%E5%89%8D%E9%A6%88%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md'>第七章——前馈神经网络</a>
                <li><a href='https://github.com/ben1234560/AiLearning-Theory-Applying/blob/master/%E4%BA%BA%E4%BA%BA%E9%83%BD%E8%83%BD%E7%9C%8B%E6%87%82%E7%9A%84Transformer/%E7%AC%AC%E5%85%AB%E7%AB%A0%E2%80%94%E2%80%94%E6%9C%80%E5%90%8E%E7%9A%84%E8%BE%93%E5%87%BA.md'>第八章——最后的输出</a>

