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https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#关于-rnn那些你不知道的事
https://github.com/km1994/NLP-Interview-Noteshttps://github.com/km1994/NLP-Interview-Notes
手机版NLP百面百搭https://mp.weixin.qq.com/s?__biz=MzAxMTU5Njg4NQ==&mid=100005719&idx=3&sn=5d8e62993e5ecd4582703684c0d12e44&chksm=1bbff26d2cc87b7bf2504a8a4cafc60919d722b6e9acbcee81a626924d80f53a49301df9bd97&scene=18#wechat_redirect
https://github.com/km1994/RES-Interview-Noteshttps://github.com/km1994/RES-Interview-Notes
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https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20210301212242.png
【关于 RNN】那些你不知道的事https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#%E5%85%B3%E4%BA%8E-rnn%E9%82%A3%E4%BA%9B%E4%BD%A0%E4%B8%8D%E7%9F%A5%E9%81%93%E7%9A%84%E4%BA%8B
一、RNN 篇https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#%E4%B8%80rnn-%E7%AF%87
1.2 为什么需要 RNN?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#12-%E4%B8%BA%E4%BB%80%E4%B9%88%E9%9C%80%E8%A6%81-rnn
1.2 RNN 结构是怎么样的？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#12-rnn-%E7%BB%93%E6%9E%84%E6%98%AF%E6%80%8E%E4%B9%88%E6%A0%B7%E7%9A%84
1.3 RNN 前向计算公式？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#13-rnn-%E5%89%8D%E5%90%91%E8%AE%A1%E7%AE%97%E5%85%AC%E5%BC%8F
1.4 RNN 存在什么问题？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#14-rnn-%E5%AD%98%E5%9C%A8%E4%BB%80%E4%B9%88%E9%97%AE%E9%A2%98
二、长短时记忆网络(Long Short Term Memory Network, LSTM) 篇https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#%E4%BA%8C%E9%95%BF%E7%9F%AD%E6%97%B6%E8%AE%B0%E5%BF%86%E7%BD%91%E7%BB%9Clong-short-term-memory-network-lstm-%E7%AF%87
2.1 为什么 需要 LSTM?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#21-%E4%B8%BA%E4%BB%80%E4%B9%88-%E9%9C%80%E8%A6%81-lstm
2.2 LSTM 的结构是怎么样的?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#22-lstm-%E7%9A%84%E7%BB%93%E6%9E%84%E6%98%AF%E6%80%8E%E4%B9%88%E6%A0%B7%E7%9A%84
2.3 LSTM 如何缓解 RNN 梯度消失和梯度爆炸问题?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#23-lstm-%E5%A6%82%E4%BD%95%E7%BC%93%E8%A7%A3-rnn-%E6%A2%AF%E5%BA%A6%E6%B6%88%E5%A4%B1%E5%92%8C%E6%A2%AF%E5%BA%A6%E7%88%86%E7%82%B8%E9%97%AE%E9%A2%98
2.3 LSTM 的流程是怎么样的?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#23-lstm-%E7%9A%84%E6%B5%81%E7%A8%8B%E6%98%AF%E6%80%8E%E4%B9%88%E6%A0%B7%E7%9A%84
2.4 LSTM 中激活函数区别?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#24-lstm-%E4%B8%AD%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0%E5%8C%BA%E5%88%AB
2.5 LSTM的复杂度？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#25-lstm%E7%9A%84%E5%A4%8D%E6%9D%82%E5%BA%A6
2.6 LSTM 存在什么问题？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#26-lstm-%E5%AD%98%E5%9C%A8%E4%BB%80%E4%B9%88%E9%97%AE%E9%A2%98
三、GRU (Gated Recurrent Unit)https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#%E4%B8%89gru-gated-recurrent-unit
3.1 为什么 需要 GRU?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#31-%E4%B8%BA%E4%BB%80%E4%B9%88-%E9%9C%80%E8%A6%81-gru
3.2 GRU 的结构是怎么样的?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#32-gru-%E7%9A%84%E7%BB%93%E6%9E%84%E6%98%AF%E6%80%8E%E4%B9%88%E6%A0%B7%E7%9A%84
3.3 GRU 的前向计算?https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#33-gru-%E7%9A%84%E5%89%8D%E5%90%91%E8%AE%A1%E7%AE%97
3.4 GRU 与其他 RNN系列模型的区别？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#34-gru-%E4%B8%8E%E5%85%B6%E4%BB%96-rnn%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%8C%BA%E5%88%AB
四、RNN系列模型篇https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#%E5%9B%9Brnn%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B%E7%AF%87
4.1 RNN系列模型 有什么特点？https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#41-rnn%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B-%E6%9C%89%E4%BB%80%E4%B9%88%E7%89%B9%E7%82%B9
参考https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#%E5%8F%82%E8%80%83
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#一rnn-篇
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#12-为什么需要-rnn
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124200244.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#12-rnn-结构是怎么样的
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124201238.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#13-rnn-前向计算公式
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124201611.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124201807.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#14-rnn-存在什么问题
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124203442.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124203827.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#二长短时记忆网络long-short-term-memory-network-lstm-篇
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#21-为什么-需要-lstm
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#22-lstm-的结构是怎么样的
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124204437.png
https://github.com/youngfire/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
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#23-lstm-的流程是怎么样的
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124204901.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124204920.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205049.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205104.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205209.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205226.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205311.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205329.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205501.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205517.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205549.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220124205602.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#24-lstm-中激活函数区别
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#25-lstm的复杂度
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#26-lstm-存在什么问题
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#三gru-gated-recurrent-unit
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#31-为什么-需要-gru
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#32-gru-的结构是怎么样的
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220125161534.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#33-gru-的前向计算
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220125162246.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220125162329.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220125162404.png
https://github.com/youngfire/NLP-Interview-Notes/blob/main/DeepLearningAlgorithm/rnn/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20220125162415.png
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#34-gru-与其他-rnn系列模型的区别
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#四rnn系列模型篇
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#41-rnn系列模型-有什么特点
https://github.com/youngfire/NLP-Interview-Notes/tree/main/DeepLearningAlgorithm/rnn#参考
循环神经网络(RNN)知识入门https://zhuanlan.zhihu.com/p/149869659
LSTM基本原理https://docs.rnn.knowledge-precipitation.site/lstm-ji-ben-yuan-li
循环神经网络 – Recurrent Neural Network | RNNhttps://easyai.tech/ai-definition/rnn/
循环神经网络--传统RNN、LSTM循序渐进的通俗易懂解释https://blog.csdn.net/weixin_40363423/article/details/89925364
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