课程目录
P1 Lecture 1.1 - What is ML什么是机器学习
P2 Lecture 1.2 - What is DL什么是深度学习
P3 Lecture 1.3 - How to Apply如何应用深度学习
P4 Lecture 2.1 - How to Train a Model如何训练模型
P5 Lecture 2.2 - What is Model什么是模型
P6 Lecture 2.3 - What does the 'Good' Function Mean什么叫做好的Function呢
P7 Lecture 2.4 - How can we Pick the 'Best' Function如何找出最好的Function
P8 Lecture 2.5 - Backpropagation效率地计算大量参数
P9 TA Recitation - Optimization
P10 Lecture 3.1 - Word Representations
P11 Lecture 3.2 - Language Modeling语言模型
P12 Lecture 3.3 - Recurrent Neural Network详细解析
P13 Lecture 3.4 - RNN Applications RNN各式应用
P14 TA Recitation - Practical Tips
P15 Lecture 4.1 - Attention Mechanism注意力机制
P16 Lecture 4.2 - Attention Applications注意力的各式应用
P17 Assignment 1 Tutorial
P18 Lecture 5.1 - Word Representation Review词向量各式表示法
P19 Lecture 5.2 - Word2Vec词向量
P20 Lecture 5.3 - Word2Vec Training如何训练词向量
P21 Lecture 5.4 - Negative Sampling
P22 Lecture 5.5 - Word2Vec Variants各种训练的变化方式
P23 Lecture 5.6 - GloVe词向量
P24 Lecture 5.7 - Word Vector Evaluation如何评价词向量的好坏
P25 Lecture 5.8 - Contextualized Word Embeddings前后文相关之词向量
P26 Lecture 5.9 - ELMo芝麻街家族之起源
P27 Lecture 6.1 - Basic Attention基本注意力模型复习
P28 Lecture 6.2 - Self Attention新注意力机制
P29 Lecture 6.3 - Multi-Head Attention
P30 Lecture 6.4- Transformer
P31 Lecture 6.5- BERT进击的芝麻街巨人
P32 TA Recitation- More on Embeddings
P33 Lecture 7.1 - Transformer-XL处理超长输入的Transformer
P34 Lecture 7.2 - XLNet兼顾AR及AE好处的模型
P35 Lecture 7.3 - RoBERTa，SpanBERT，XLM简单有用的改进方法
P36 Lecture 7.4- ALBERT如何让BERT缩小却依然好用呢
P37 TA Recitation - More on Transformers
P38 Lecture 8.1- Deep Reinforcement Learning Introduction
P39 Lecture 8.2- Markov Decision Process
P40 Lecture 8.3- Reinforcement Learning
P41 Lecture 8.4- Value-Based RL Approach
P42 Lecture 8.5- Advanced DQN
P43 Lecture 9.1- Policy Gradient
P44 Lecture 9.2- Actor Critic
P45 Lecture 10.1- Natural Language Generation
P46 Lecture 10.2- Decoding Algorithm
P47 Lecture 10.3- NLG Evaluation
P48 Lecture 10.4- RL for NLG（20-05-12）
P49 TA Recitation- RL for Dialogues
P50 GAN（Quick Review）
P51 GAN Lecture 4（2018）- Basic Theory
P52 GAN Lecture 6（2018）- WGAN，EBGAN
P53 Lecture 11.1- Unsupervised Learning Introduction
P54 Lecture 11.2- Autoencoder & Variational Autoencoder
P55 Lecture 11.3- Distant Supervision & Multi-Task Learning
P56 Lecture 12.1- Conversational AI Introduction对话AI简介
P57 Lecture 12.2- Task-Oriented Dialogues任务型对话
P58 Lecture 12.3- Chit-Chat Social Bots聊天型对话
P59 Lecture 13.1- Robustness对话系统的强健性
P60 Lecture 13.2- Scalability对话系统的扩展性
P61 Final Project- Rules & Grading
P62 Career Sharing求学经验分享
