简要说明
这是我在学习李弘毅老师的2020春季课程【Deep Learning for Human Language Processing】时做的课程笔记。写课程笔记的初衷是为了帮助自己之后快速的回顾复习,因为我记性不好,不做笔记的话,过不了1个月就忘了大半了。然后写着写着发现,有些内容虽然听时觉得自己懂了,但是要自己讲(写)出来的时候,自己其实是不太懂的,于是就会去找些其他的资料来把这部分知识给补上。所以,就决定一直写下去了。
当然,这个作为博客发出来也是方便其他对课程感兴趣的同学的复习和探讨。有不足之处,请务必直接指正。
这篇是所有课程笔记的索引。
已完结。
课程目录
【Speech Recognition】
- 1-1 overview
- 1-2 LAS
- 1-3 CTC, RNN-T and more
- 1-4 HMM
- 1-5 Alignment of HMM, CTC and RNN-T
- 1-6 RNN-T Training
- 1-7 Language Modeling
【Voice Conversion】
- 2-1 Feature Disentangle
- 2-2 CycleGAN and StarGAN
【Speech Separation】
- 3-1 Deep Clustering, PIT
- 3-2 TasNet
【Speech_Synthesis】
- 4-1 Tacotron
- 4-2 More than Tacotron
【Speaker Verification】
- 5 Speaker Verification
【Vocoder】
- 6 Vocoder
【NLP】
- 7-1 Overview of NLP Tasks
- 7-2 BERT and its family - Introduction and Fine-tune
- 7-3_BERT and its family - ELMo, BERT, GPT, XLNet, MASS, BART, UniLM, ELECTRA, and more
- 7-4 來自獵人暗黑大陸的模型 GPT-3
- 7-5 Multilingual BERT
- 7-6 Text Style Transfer
- 7-7 Deep Learning for Coreference Resolution
- 7-8 Deep Learning for Constituency Parsing
- 7-9 Deep Learning for Dependency Parsing
- 7-10 Deep Learning for Question Answering (1/2)
- 7-11 Deep Learning for Question Answering (2/2)
- 7-12 Controllable Chatbot
- 7-13 Dialogue State Tracking (as Question Answering)
【Others】
- 搞懂RNN
- 搞懂Transformer
完结日期:2021-03-20