Yanjun Qi
This Lecture slides:
https://qiyanjun.github.io/2020f-UVA-CS-MachineLearningDeep//Lectures/S3-deepNNtext.pdf
Lecture 18 covers:
What is NLP?
Typical NLP tasks / Challenges / Pipeline
f() on natural language
Before Deep NLP (Pre 2012) • (BOW / LSI / Topic Modeling LDA )
Word2Vec (2013-2016) • (GloVe/ FastText)
Recurrent NN (2014-2016) • LSTM
Seq2Seq
Attention /
Self-Attention (2016 – now )
Transformer (attention only Seq2Seq)
BERT / RoBERTa/ XLNet/ GPT-2 / …
Course Video PlayList:
https://www.youtube.com/watch?v=4tGQiWXlsLM&list=PLHj6AQSqIUkOiQ6DfCOjs1Jc7ltXwuG9Z
Course Web:
https://qiyanjun.github.io/2020f-UVA-CS-MachineLearningDeep/
Machine Learning is concerned with building computer programs that automatically improve through experience. This 3-credit course covers master-level topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning (especially modern deep learning), unsupervised learning, learning theory, and RL.
+ Assignments include multiple short programming and writing assignments for hands-on experiments of various learning algorithms, multiple in-class quizzes, and a final project.
+ Objective of this course:
+ Goal: To help students get capable in building machine learning tools (not just a tool user!!!)
+ Key Results: (1) to build multiple machine learning methods from scratch, (2) to understand complex machine learning methods at the source code level and (3) to produce one machine learning project on cutting-edge data applications with health or social impacts or with cutting-edge engineering impacts on deep learning benchmarking libraries.