Coursera 笔记

DeepLearning.AI

Sequence Models

序列模型

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Module 01

Recurrent Neural Networks

16 / 21 篇
  1. 01Why Sequence Models?序列模型介绍
  2. 02Notation序列模型的符号表示
  3. 03Recurrent Neural Network Model循环神经网络模型
  4. 04Backpropagation Through Time通过时间的反向传播
  5. 05Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!
  6. 06Different Types of RNNsRNN 的不同类型
  7. 07Language Model and Sequence Generation语言模型与序列生成
  8. 08Sampling Novel Sequences采样生成新序列
  9. 09Vanishing Gradients with RNNsRNN 的梯度消失
  10. 10Clarifications about Upcoming Gated Recurrent Unit (GRU) Video
  11. 11Gated Recurrent Unit (GRU)门控循环单元
  12. 12Clarifications about Upcoming Long Short Term Memory (LSTM) Video
  13. 13Long Short Term Memory (LSTM)长短期记忆网络
  14. 14Bidirectional RNN双向 RNN
  15. 15Deep RNNs深度 RNN
  16. 16Lecture Notes W1
  17. 17Recurrent Neural Networks循环神经网络测验
  18. 18(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
  19. 19Building your Recurrent Neural Network - Step by Step逐步构建循环神经网络
  20. 20Dinosaur Island-Character-Level Language Modeling恐龙岛：字符级语言建模
  21. 21Jazz Improvisation with LSTM用 LSTM 即兴演奏爵士乐
Module 02

Natural Language Processing & Word Embeddings

13 / 15 篇
  1. 01Word Representation词汇表示
  2. 02Using Word Embeddings使用词嵌入
  3. 03Properties of Word Embeddings词嵌入的特性
  4. 04Embedding Matrix嵌入矩阵
  5. 05Learning Word Embeddings学习词嵌入
  6. 06Word2VecWord2Vec
  7. 07Negative Sampling负采样
  8. 08Clarifications about Upcoming GloVe Word Vectors Video
  9. 09GloVe Word VectorsGloVe 词向量
  10. 10Sentiment Classification情感分类
  11. 11Debiasing Word Embeddings词嵌入去偏
  12. 12Lecture Notes W2
  13. 13Natural Language Processing & Word Embeddings自然语言处理与词嵌入测验
  14. 14Programming Assignment: Operations on Word Vectors - Debiasing编程作业：词向量运算与去偏
  15. 15Emojify表情符号化
Module 03

Sequence Models & Attention Mechanism

14 / 16 篇
  1. 01Basic Models序列到序列基本模型
  2. 02Picking the Most Likely Sentence选择最可能的句子
  3. 03Beam Search束搜索
  4. 04Refinements to Beam Search束搜索的改进
  5. 05Error Analysis in Beam Search束搜索的错误分析
  6. 06Bleu Score (Optional)BLEU 分数（可选）
  7. 07Attention Model Intuition注意力模型直观理解
  8. 08Clarifications about Upcoming Attention Model Video
  9. 09Attention Model注意力模型
  10. 10Speech Recognition语音识别
  11. 11Trigger Word Detection触发词检测
  12. 12Lecture Notes W3
  13. 13Sequence Models & Attention Mechanism序列模型与注意力机制测验
  14. 14Neural Machine Translation神经机器翻译
  15. 15Instructions If You Are Unable to Open Your Notebook无法打开 Notebook 时的处理说明
  16. 16Trigger Word Detection触发词检测（编程作业）
Module 04

Transformer Network

12 / 15 篇
  1. 01Transformer Network IntuitionTransformer 网络直观理解
  2. 02Self-Attention自注意力
  3. 03Multi-Head Attention多头注意力
  4. 04Transformer NetworkTransformer 网络
  5. 05Lecture Notes W4
  6. 06TransformersTransformer 测验
  7. 07[IMPORTANT] Reminder about end of access to Lab Notebooks
  8. 08Transformers Architecture with TensorFlow基于 TensorFlow 的 Transformer 架构
  9. 09Transformer Pre-processingTransformer 预处理
  10. 10Transformer Network Application: Named-Entity RecognitionTransformer 网络应用：命名实体识别
  11. 11Transformer Network Application: Question AnsweringTransformer 网络应用：问答
  12. 12Conclusion and Thank You!课程总结与致谢
  13. 13References参考文献
  14. 14Acknowledgments
  15. 15(Optional) Opportunity to Mentor Other Learners（可选）担任其他学员导师的机会