本文主要是介绍【Reading List】【20190510】预训练（pre-trained）语言模型，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
RNN，seq2seq，Attention：
https://www.leiphone.com/news/201709/8tDpwklrKubaecTa.html
图解transformer ：
https://blog.csdn.net/qq_41664845/article/details/84969266
Attentinon：
https://blog.csdn.net/malefactor/article/details/50550211
ELMO、GPT、BERT对比：
https://www.cnblogs.com/robert-dlut/p/9824346.html
GPT
官网：https://openai.com/blog/language-unsupervised/
解析：https://blog.csdn.net/Magical_Bubble/article/details/89497002
GPT2
官网：https://openai.com/blog/better-language-models/#update
解析：https://blog.csdn.net/Magical_Bubble/article/details/89499275
官方资源：https://github.com/openai/gpt-2
https://github.com/openai/gpt-2-output-dataset
git：https://github.com/oldyang95/GPT2-Models/
Tensorflow_GPU与Cuda对应关系：
https://blog.csdn.net/u011748542/article/details/85090268
论文：
(GPT)Improving Language Understanding by Generative Pre-Training
(GPT-2)Language Models are Unsupervised Multitask Learners
Attention Is All You Need
分享：Deep Graph Convolutional Encoders for Structured Data to Text Generation
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