《PyTorch深度学习指南：序列与自然语言处理》札记.docx

《PyTorch深度学习指南：序列与自然语言处理》读书随笔
目录
一、内容概览................................................2
1.1书籍简介.............................................3
1.2作者介绍.............................................4
1.3本书目的.............................................4
二、PyTorch简介.............................................5
2.1PyTorch的发展历程....................................7
2.2PyTorch的特点........................................8
2.3PyTorch的应用场景...................................10
三、深度学习基础...........................................11
3.1机器学习基本概念....................................12
3.2深度学习基本概念....................................13
3.3神经网络的基本结构..................................14
四、PyTorch基础知识........................................15
五、PyTorch进阶知识........................................16
六、序列数据处理...........................................18
6.1文本数据预处理......................................19
6.2周期性序列处理......................................21
6.3非周期性序列处理....................................22
七、自然语言处理...........................................24
7.1NLP基本任务.........................................25
7.2基于Transformer的模型...............................26
7.3BERT模型介绍........................................28
7.4GPT模型介绍.........................................30
八、深度学习模型应用案例...................................31
8.1机器翻译............................................32
8.2文本分类............................................32
8.3情感分析............................................34
8.4问答系统............................................35
九、深度学习模型调优.......................................36
9.1学习率调整策略......................................37
9.2正则化技术..........................................38
9.3批量归一化..........................................39
9.4模型集成............................................40
十、总结与展望.............................................41
10.1本书总结...........................................42
10.2未来发展趋势.......................................44
10.3学习建议..........................................