Coursera 笔记

DeepLearning.AI/Sequence Models/Transformers

Sequence Models

序列模型 · DeepLearning.AI · 55 篇

01Recurrent Neural Networks16

01Why Sequence Models?序列模型介绍
02Notation序列模型的符号表示
03Recurrent Neural Network Model循环神经网络模型
04Backpropagation Through Time通过时间的反向传播
05Different Types of RNNsRNN 的不同类型
06Language Model and Sequence Generation语言模型与序列生成
07Sampling Novel Sequences采样生成新序列
08Vanishing Gradients with RNNsRNN 的梯度消失
09Gated Recurrent Unit (GRU)门控循环单元
10Long Short Term Memory (LSTM)长短期记忆网络
11Bidirectional RNN双向 RNN
12Deep RNNs深度 RNN
13Recurrent Neural Networks循环神经网络测验
14Building your Recurrent Neural Network - Step by Step逐步构建循环神经网络
15Dinosaur Island-Character-Level Language Modeling恐龙岛：字符级语言建模
16Jazz Improvisation with LSTM用 LSTM 即兴演奏爵士乐

02Natural Language Processing & Word Embeddings13

01Word Representation词汇表示
02Using Word Embeddings使用词嵌入
03Properties of Word Embeddings词嵌入的特性
04Embedding Matrix嵌入矩阵
05Learning Word Embeddings学习词嵌入
06Word2VecWord2Vec
07Negative Sampling负采样
08GloVe Word VectorsGloVe 词向量
09Sentiment Classification情感分类
10Debiasing Word Embeddings词嵌入去偏
11Natural Language Processing & Word Embeddings自然语言处理与词嵌入测验
12Programming Assignment: Operations on Word Vectors - Debiasing编程作业：词向量运算与去偏
13Emojify表情符号化

03Sequence Models & Attention Mechanism14

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

04Transformer Network12

01Transformer Network IntuitionTransformer 网络直观理解
02Self-Attention自注意力
03Multi-Head Attention多头注意力
04Transformer NetworkTransformer 网络
05TransformersTransformer 测验
06Transformers Architecture with TensorFlow基于 TensorFlow 的 Transformer 架构
07Transformer Pre-processingTransformer 预处理
08Transformer Network Application: Named-Entity RecognitionTransformer 网络应用：命名实体识别
09Transformer Network Application: Question AnsweringTransformer 网络应用：问答
10Conclusion and Thank You!课程总结与致谢
11References参考文献
12(Optional) Opportunity to Mentor Other Learners（可选）担任其他学员导师的机会

Module 04 · Transformer Network

TransformersTransformer 测验

← 上一篇Transformer NetworkTransformer 网络下一篇 →Transformers Architecture with TensorFlow基于 TensorFlow 的 Transformer 架构

Coursera 笔记 · Noted by 千啾略 · As of 2026-09-14