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rnn 更新
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km1994 committed Jan 25, 2022
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23 changes: 23 additions & 0 deletions DeepLearningAlgorithm/rnn/readme.md
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> **关注公众号 【关于NLP那些你不知道的事】 加入 【NLP && 推荐学习群】一起学习！！！**
- [【关于 RNN】那些你不知道的事](#关于-rnn那些你不知道的事)
- [一、RNN 篇](#一rnn-篇)
- [1.2 为什么需要 RNN?](#12-为什么需要-rnn)
- [1.2 RNN 结构是怎么样的？](#12-rnn-结构是怎么样的)
- [1.3 RNN 前向计算公式？](#13-rnn-前向计算公式)
- [1.4 RNN 存在什么问题？](#14-rnn-存在什么问题)
- [二、长短时记忆网络(Long Short Term Memory Network, LSTM) 篇](#二长短时记忆网络long-short-term-memory-network-lstm-篇)
- [2.1 为什么 需要 LSTM?](#21-为什么-需要-lstm)
- [2.2 LSTM 的结构是怎么样的?](#22-lstm-的结构是怎么样的)
- [2.3 LSTM 如何缓解 RNN 梯度消失和梯度爆炸问题?](#23-lstm-如何缓解-rnn-梯度消失和梯度爆炸问题)
- [2.3 LSTM 的流程是怎么样的?](#23-lstm-的流程是怎么样的)
- [2.4 LSTM 中激活函数区别?](#24-lstm-中激活函数区别)
- [2.5 LSTM的复杂度？](#25-lstm的复杂度)
- [2.6 LSTM 存在什么问题？](#26-lstm-存在什么问题)
- [三、GRU (Gated Recurrent Unit)](#三gru-gated-recurrent-unit)
- [3.1 为什么 需要 GRU?](#31-为什么-需要-gru)
- [3.2 GRU 的结构是怎么样的?](#32-gru-的结构是怎么样的)
- [3.3 GRU 的前向计算?](#33-gru-的前向计算)
- [3.4 GRU 与其他 RNN系列模型的区别？](#34-gru-与其他-rnn系列模型的区别)
- [四、RNN系列模型篇](#四rnn系列模型篇)
- [4.1 RNN系列模型 有什么特点？](#41-rnn系列模型-有什么特点)
- [参考](#参考)

## 一、RNN 篇

### 1.2 为什么需要 RNN?
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23 changes: 23 additions & 0 deletions README.md
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- [3.3 训练过程](DeepLearningAlgorithm/adversarial_training_study/readme.md#33-训练过程)
- [3.4 训练所涉及相关理论基础](DeepLearningAlgorithm/adversarial_training_study/readme.md#34--训练所涉及相关理论基础)
- [四、总结](DeepLearningAlgorithm/adversarial_training_study/readme.md#四总结)
- [【关于 RNN】那些你不知道的事](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#关于-rnn那些你不知道的事)
- [一、RNN 篇](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#一rnn-篇)
- [1.2 为什么需要 RNN?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#12-为什么需要-rnn)
- [1.2 RNN 结构是怎么样的？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#12-rnn-结构是怎么样的)
- [1.3 RNN 前向计算公式？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#13-rnn-前向计算公式)
- [1.4 RNN 存在什么问题？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#14-rnn-存在什么问题)
- [二、长短时记忆网络(Long Short Term Memory Network, LSTM) 篇](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#二长短时记忆网络long-short-term-memory-network-lstm-篇)
- [2.1 为什么 需要 LSTM?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#21-为什么-需要-lstm)
- [2.2 LSTM 的结构是怎么样的?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#22-lstm-的结构是怎么样的)
- [2.3 LSTM 如何缓解 RNN 梯度消失和梯度爆炸问题?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#23-lstm-如何缓解-rnn-梯度消失和梯度爆炸问题)
- [2.3 LSTM 的流程是怎么样的?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#23-lstm-的流程是怎么样的)
- [2.4 LSTM 中激活函数区别?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#24-lstm-中激活函数区别)
- [2.5 LSTM的复杂度？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#25-lstm的复杂度)
- [2.6 LSTM 存在什么问题？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#26-lstm-存在什么问题)
- [三、GRU (Gated Recurrent Unit)](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#三gru-gated-recurrent-unit)
- [3.1 为什么 需要 GRU?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#31-为什么-需要-gru)
- [3.2 GRU 的结构是怎么样的?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#32-gru-的结构是怎么样的)
- [3.3 GRU 的前向计算?](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#33-gru-的前向计算)
- [3.4 GRU 与其他 RNN系列模型的区别？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#34-gru-与其他-rnn系列模型的区别)
- [四、RNN系列模型篇](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#四rnn系列模型篇)
- [4.1 RNN系列模型 有什么特点？](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#41-rnn系列模型-有什么特点)
- [参考](https://github.com/km1994/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#参考)


### 四、[【关于 NLP 学习算法】那些你不知道的事](NLPinterview)

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