# 第五门课 序列模型(Sequence Models)

- [第一周 循环序列模型（Recurrent Neural Networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks.md)
- [1.1 为什么选择序列模型？（Why Sequence Models?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/11-wei-shi-yao-xuan-ze-xu-lie-mo-xing-ff1f-ff08-why-sequence-models.md)
- [1.2 数学符号（Notation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/12-shu-xue-fu-hao-ff08-notation.md)
- [1.3 循环神经网络模型（Recurrent Neural Network Model）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/13-xun-huan-shen-jing-wang-luo-mo-xing-ff08-recurrent-neural-network-model.md)
- [1.4 通过时间的反向传播（Backpropagation through time）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/14-tong-guo-shi-jian-de-fan-xiang-chuan-bo-ff08-backpropagation-through-time.md)
- [1.5 不同类型的循环神经网络（Different types of RNNs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/15-bu-tong-lei-xing-dexun-huan-shen-jing-wang-luo-ff08-different-types-of-rnns.md)
- [1.6 语言模型和序列生成（Language model and sequence generation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/16-yu-yan-mo-xinghe-xu-liesheng-cheng-ff08-language-model-and-sequence-generation.md)
- [1.7 对新序列采样（Sampling novel sequences）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/17-dui-xin-xu-lie-cai-yang-ff08-sampling-novel-sequences.md)
- [1.8 循环神经网络的梯度消失（Vanishing gradients with RNNs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/18-xun-huan-shen-jing-wangluo-de-ti-du-xiao-shi-ff08-vanishing-gradients-with-rnns.md)
- [1.9 GRU单元（Gated Recurrent Unit（GRU））](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/19-grudan-yuan-ff08-gated-recurrent-unit-gru.md)
- [1.10 长短期记忆（LSTM（long short term memory）unit）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/110-chang-duan-qi-ji-yi-ff08-lstm-long-short-term-memory-unit.md)
- [1.11 双向循环神经网络（Bidirectional RNN）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/111-shuang-xiang-xun-huan-shenjing-wang-luo-ff08-bidirectional-rnn.md)
- [1.12 深层循环神经网络（Deep RNNs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/112-shen-ceng-xun-huan-shen-jing-wang-luo-ff08-deep-rnns.md)
- [Building your Recurrent Neural Network](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/building+a+recurrent+neural+network+-+step+by+step+-+v3.md)
- [rnn\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/rnnutils-py.md)
- [Dinosaurus Island -- Character level language model final](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/dinosaurus+island+-+character+level+language+model+final+-+v3.md)
- [utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/utilspy.md)
- [shakespeare\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/shakespeareutils-py.md)
- [Improvise a Jazz Solo with an LSTM Network](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/improvise+a+jazz+solo+with+an+lstm+network+-+v3.md)
- [第二周 自然语言处理与词嵌入（Natural Language Processing and Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings.md)
- [2.1 词汇表征（Word Representation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/21-ci-hui-biao-zheng-ff08-word-representation.md)
- [2.2 使用词嵌入（Using Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/22-shi-yong-ci-qian-ru-ff08-using-word-embeddings.md)
- [2.3 词嵌入的特性（Properties of Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/23-ci-qian-ru-de-texing-ff08-propertiesof-word-embeddings.md)
- [2.4 嵌入矩阵（Embedding Matrix）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/24-qian-ru-ju-zhen-ff08-embedding-matrix.md)
- [2.5 学习词嵌入（Learning Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/25-xue-xi-ci-qian-ru-ff08-learning-word-embeddings.md)
- [2.6 Word2Vec](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/26-word2vec.md)
- [2.7 负采样（Negative Sampling）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/27-fu-cai-yang-ff08-negative-sampling.md)
- [2.8 GloVe 词向量（GloVe Word Vectors）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/28-glove-ci-xiang-liang-ff08-glove-word-vectors.md)
- [2.9 情感分类（Sentiment Classification）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/29-qing-ganfen-lei-ff08-sentiment-classification.md)
- [2.10 词嵌入除偏（Debiasing Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/210-ci-qian-ru-chu-pian-ff08-debiasing-word-embeddings.md)
- [Operations on word vectors](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/operations+on+word+vectors+-+v2.md)
- [w2v\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/w2vutils-py.md)
- [Emojify](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/emojify-v2.md)
- [emo\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/emoutils-py.md)
- [第三周 序列模型和注意力机制（Sequence models & Attention mechanism）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism.md)
- [3.1 基础模型（Basic Models）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/31-ji-chu-mo-xing-ff08-basic-models.md)
- [3.2 选择最可能的句子（Picking the most likely sentence）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/32-xuan-ze-zuike-neng-de-ju-zi-ff08-picking-the-most-likely-sentence.md)
- [3.3 集束搜索（Beam Search）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/33-ji-shu-sou-suo-ff08-beam-search.md)
- [3.4 改进集束搜索（Refinements to Beam Search）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/34-gai-jin-ji-shu-sou-suo-ff08-refinements-to-beam-search.md)
- [3.5 集束搜索的误差分析（Error analysis in beam search）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/35-ji-shu-sou-suode-wu-cha-fen-xi-ff08-error-analysis-in-beam-search.md)
- [3.6 Bleu 得分（选修）（Bleu Score (optional)）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/36-bleu-de-fen-ff08-xuan-xiu-ff09-ff08-bleu-score-optional.md)
- [3.7 注意力模型直观理解（Attention Model Intuition）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/37-zhu-yi-li-mo-xing-zhi-guan-li-jie-ff08-attention-model-intuition.md)
- [3.8注意力模型（Attention Model）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/38zhu-yi-li-mo-xing-ff08-attention-model.md)
- [3.9语音识别（Speech recognition）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/39yu-yin-shi-bieff08-speech-recognition.md)
- [3.10触发字检测（Trigger Word Detection）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/310hong-fa-zi-jian-ce-ff08-trigger-word-detection.md)
- [Neural machine translation with attention](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/neural-machine-translation-with-attention-v4.md)
- [nmt\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/nmtutils-py.md)
- [Trigger word detection](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/trigger-word-detection-v1.md)
- [td\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/tdutils-py.md)


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