|
Date |
Topics |
Related Materials
and
Resources
|
Assignments |
1 |
3/15-3/21 |
Introduction to Computational
Linguistics / Natural Language Processing
Preliminaries:
|
PyTorch:
|
Install Python
3.x and PyTorch
모두를 위한 머신러닝/딥러닝(홍콩과기대 김성훈
교수)
- Linear
Regression의 개념:비디오, 강의 슬라이드
- Linear
Regression cost함수 최소화: 비디오, 강의 슬라이
- 여러 개의
입력(feature)의 Linear Regression: 비디오, 강의 슬라이드
- Logistic
Regression classification: 강의 슬라이드-Hypothesis 함수 소개:
비디오-
cost 함수 소개: 비디오
How to
Implement Simple Linear Regression From
Scratch with Python
Logistic
Regression for Machine Learning
|
2 |
3/22-3/28 |
Introduction
to a Neural Network
|
PyTorch:
|
딥러닝 개념잡기
|
3 |
3/29-4/4 |
Introduction to a Neural Network
|
PyTorch:
|
Hw1: Logistic
Regression: Diabetes Data
|
4 |
4/5-4/11 |
Introduction to a Neural
Network
- Parameter Optimization
- Weight Decay
- Batch Normalization
- DropOut
Hyper-parameter
Tuning Techniques in Deep Learning
An
Overview of Gradient Descent Optimization
Algorithm
|
PyTorch:
|
Hw2: How To
Prepare Movie Review Data For Sentiment Analysis
|
5 |
4/12-4/18 |
Introduction to a Neural
Network
- Parameter Optimization
- Weight Decay
- Batch Normalization
- DropOut
Hyper-parameter
Tuning Techniques in Deep Learning
An
Overview of Gradient Descent Optimization
Algorithm
|
PyTorch:
|
Hw3:
IMDB_TwoLayerNet
|
6 |
4/19-4/25 |
Convolutional Neural
Network
Understanding
Convolutional Neural Network for NLP
|
PyTorch:
|
|
7 |
4/26-4/25 |
Reccurent Neural Network
A Friendly Introduction to
Recurrent Neural Network
Long
Short-Term Memory Neural Network and Gated
Recurrent Unit
Mid-Term Test
|
PyTorch:
|
MidTermProject:
Korean News Data Torch FNN
|
8 |
5/3-5/9 |
NLP Task 1: Sentiment
Analysis
PyTorch Sentiment
Analysis (IMDB)
NLP Task 2: Sentiment Analysis for Korean
Naver Movie Review Sentiment Analysis
|
A Comprehensive
Introduction to Torchtext
Torchtext
Github
|
|
9
|
5/10-5/16 |
Encoder-Decoder
Encoder-Decoder
Long Short-Term Memory Networks
A
Gentle Introduction to LSTM Autoencoders
Step-by-step
Understanding LSTM Autoencoder layers
|
PyTorch:
|
|
10
|
5/17-5/23
|
Attention
Model
Neural
Machine Translation By Jointly Learning to Align
and Translate
Attention:
Illustrated Attention
Attention and Memory in
Deep Learning and NLP
|
PyTorch:
Translation with Sequence to
Sequence Network and Attention |
|
11 |
5/24-5/30 |
Transformer
Self Attention: Attention
is All you need
The
Illustrated Transformer
Seq2Seq
Pay Attention to Self Attention: Part I
Seq2seq
Pay Attention to Self Attention: Part 2
|
PyTorch:
Translation with Sequence to
Sequence Network and Attention
PyTorch-Transformers
by Huggingface and Full
Documentation
|
|
12 |
5/31-6/6
|
BERT
(Bidirectional Encoder Representations from
Transformers)
XLNet
What is XLNet and
why it outperforms BERT?
XLNet - a clever
language modeling solution
XLNET -SOTA
pre-training method that outperforms
BERT
XLM
XLM - Enhancing
BERT for Cross-lingual Language Model
FastBert
Introducing
FastBert - A Simple Deep Learning Library for
BERT Models
RoBERTa
RoBERTa:
A Robustly Optimized BERT Pretaining Approach
RoBERTa:
An optimized method for self-supervised NLP
systems
Distilling
BERT - How to achieve BERT performance using
Logistic Regression
Meet
ALBERT: a new 'Lite BERT' from Google &
Toyota With State of the ART NLP performance
and 18x fewer parameters
BERT,
RoBERTa, DIstilBERT, XLNet - which one to use?
|
PyTorch:
The Annotated Transformer
BERT Fine Tuning
BERT Fine-Tuning
Tutorial with PyTorch
Painless
Fine-Tuning of BERT in Pytorch
The Latest
Breakthroughs and Developments
in Natural Language Processing
|
|
13 |
6/6-6/13 |
Embeddings (word embeddings)
Sebastian Ruder의 On word
Embeddings Part1, 2, 3, 4:
A
hands-on Intuitive Approach to Deep Learning
Methods for Text Data - Word2Vec, Glove, and
FastText
The
Current Best of Universal Word Embeddings and
Sentence Embeddings
Notebook
|
PyTorch:
Word
Embeddings: Encoding Lexical Semantics
BERT Word Embedding
|
|
14 |
6/14-6/20 |
Final Test
|
|
|