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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)
- dnn_mnist.py
- 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)
- cnn_mnist.py
- Creating CNN with TF [Video Lecture 08]
- HW3 (due June 15, by 12:00pm @ Cluster Bd. #620. No late submission will be accepted)
- autocomplete.py
- 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]
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