Course Logistics
Applied Deep Learning
February 22nd, 2021 http://adl.miulab.tw
Course Logistics
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Instructor: 陳縕儂 Yun-Nung (Vivian) Chen◉
Time: Monday 234, 9:10-12:20◉
Location: 資103◉
Website: http://adl.miulab.tw◉
NTU COOL: https://cool.ntu.edu.tw/courses/4591◉ Email: [email protected]
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To ensure timely response, email title should contain “[ADL2021]”○
Do NOT send to our personal emails2
Always check the up-to-date information from the course website
NTU COOL for Fighting Coronavirus
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NTU COOL○
Lecture videos■ Comments anytime
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Assignment submission (還可以寫 code 呢!)◉
Slido QA○
#ADL2021◉
TA Team○
Forum discussion (preferred)○
Email QA○
TA recitation/hours3
Course Goal
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The students are expected to understand1.
how deep learning works2. how to frame tasks into learning problems
3.
how to use toolkits to implement designed models, and4.
when and why specific deep learning techniques work for specific problems4
Pre-requisites
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Course○
Required: college-level calculus, linear algebra○
Preferred: probability, statistics◉
Programming○
proficiency in Python; all assignments will be in Python○
GitHub; all assignments will be handed in via GitHub○
Kaggle; all assignments will be submitted to Kaggle5
(tutorialfrom Stanford) (tutorial) (website)
Please consider your available resources for taking this course
GitHub Student Pack
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The student plan provides unlimited private repositories○
make your assignments private before the due date○
make them public afterwards6
Grading Policy
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3 Individual Assignment: 20% x 3 = 60%○
GitHub code w/ README■
The score is based on coding and the report■
Bonus points for outstanding performance■
Late policy: 25% off per day late afterwards◉
Final Group Project: 30%○
GitHub code, Project document■
Bonus points for the outstanding work○
Final presentation (format TBA)◉
Participation: 10%○
Forum/slido discussion involvement○
Write-up for the special events7
Understanding the difference between “collaboration” and “academic infraction”
Individual Assignments
8
A1. Sequence Tagging A2. Transformer / BERT A3. Language Generation
Final Group Project (2~5 persons)
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The final project topic will be announced later○
Presentation■
Poster or oral presentation○
Project Report & Code■
Wrap-up project report■
GitHub code submission w/ README 9The project details will be announced later
How to Get the Registration Code?
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Limit: 120 students per course○
72 registered◉ Requirements
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Available GPU Resources○
Programming skills○
Finish HW0○
Fill in the Google Form10
Tentative Schedule
11
Week Topic Assignment
1 2021/02/22 Course Logistics & Introduction A0 – Pytorch Tutorial 2021/03/01 Break
2 2020/03/08 NN Basics & Backpropagation
3 2020/03/15 Word Representations + RNN A1 – Summarization 4 2020/03/22 Attention
5 2020/03/29 Word Embeddings + ELMo 2020/04/05 Break
6 2020/04/12 Transformer + BERT A2 – BERT
2020/04/19 Midterm Break 7 2020/04/26 More BERT
8 2020/05/03 RL Intro + Basic Q-Learning
9 2020/05/10 Policy Gradient + Actor-Critic A3 – NLG 10 2020/05/17 Natural Language Generation
11 2020/05/24 Special Topic: Conversational AI Final Project 12 2020/05/31 TBA
13 2020/06/07 TBA 2020/06/14 Break
14 2020/06/21 Final Project Presentation
Teaching Assistant Team
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Rules
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Asking online questions is encouraged!!
Any comment or feedback is preferred!!
(speed, style, etc)
Attending TA hours!! (details TBA)
Any questions ?
You can find the course information at
◉ http://adl.miulab.tw
◉ slido: #ADL2021
◉ YouTube: Vivian NTU MiuLab
Thanks!
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