2018-1 Deep Learning
Time: Wed 3:00-4:30pm, Fri (online lecture)
Location: Cluster Bd. R509 (학연산클러스터 509호)
Textbook: Deep Learning, Goodfellow, Bengio, Courville, MIT Press, 2016
Grading:
Homework: 60%
Project: 30%
Attendance: 10% (5% online + 5% offline)
Project Timeline
Brainstorming (May 16)
Project Proposal (submission due May 25, 1pm): 2 pages
Motivation
Problem description (Data, Method, etc)
In-class project check (May 30)
No class (June 6: memorial day, June 13: election day)
Project presentation (June 14): 5 mins per team
16:30 ~ 17:30 @ Eng Bd #1 (1공학관), R.305
Final report (due June 22, 1pm): 10 pages
Project data suggetions
Submit Proposal
*The form of E-mail head,title must be like this.
(if you do not keep that form, TA will not read.)
[cse4048][Student ID][name] title
Homeworks
HW1 (due April 27 by 12:00pm @ Cluster Bd. #620. No late submission will be accepted)
Creating DNN with Tensorflow [Video Lecture 07]
Task 2: you can assume that the keys for hidden layers in the dictionary start with the character 'h'
HW2 (due June 1, by 12:00pm @ Cluster Bd. #620. No late submission will be accepted)
Creating CNN with TF [Video Lecture 08]
HW3 (due June 15, by 12:00pm @ Cluster Bd. #620. No late submission will be accepted)
Creating RNN with TF [Video Lecture 09]
Lecture Videos
1. Basic Math (link)
2. Probability (link)
3. MLE (link)
4. Training problem (link)
5. SGD (link)
6. Back-propagation (link)
7. DNN with TF (link)
8. CNN with TF (link)
9. RNN with TF (link)
10. Activation function 1 (link)
11. Activation function 2 (link)
12. Pre-processing (link)
13. Dropout & batch-normalization (link)
Lecture Notes
Lecture 01. Introduction [pdf]
Lecture 02. Machine learning basics [pdf]
Lecture 03. Multi-Layer Perceptron [pdf]
Lecture 04. Introduction to Tensorflow [pdf]
Lecture 05. Tensorflow: linear regression, logistic regression [code]
Lecture 06. Convolutional neural network 1 [pdf]
Lecture 07. Convolutional neural network 2 [pdf]
No lecture on April 27
Make-up lecture on May 2, 1:30~3:00 @ usual classroom (Cluster Bd. PBL Purple)
Lecture 08-09. Recurrent neural network [pdf]
Lecture 10. Advanced Topics: GPU, Initialization, Hyperparameter Tuning [pdf]
Lecture 11. Project discussion
Lecture 12. Project discussion
Lecture 13. Project check
Lecture 14 (June 14) 16:30 ~ 17:30 @ Eng Bd #1, R.305
Advanced Topics: Activation functions, Dropout [pdf]