2019-1 Deep Learning

Time: Tue 10:30-12:00pm

Location: Cluster Bd. (학연산클러스터) Buzzing Blue Room (5th floor)

Textbook: Deep Learning, Goodfellow, Bengio, Courville, MIT Press, 2016

Grading:

    • Homeworks, Midterm, Final Exam = 30%, 30%, 30%
    • Attendance: 10% (offline)
    • Midterm grades


Homeworks

Lecture Notes

  • Lecture 1. Introduction (pdf)
  • Lecture 2. Intro. to Machine Learning & Deep Learning (pdf)
  • Lecture 3. Multi-layer perceptron (pdf)
  • Lecture 4. Intro to Tensorflow (pdf, code)
  • Lecture 5. Convolutional Neural Networks I (pdf)
    • V5 . Training optimization problems (video, pdf)
    • HW1 is out!
  • Lecture 6. Convolutional Neural Networks II (pdf)
  • Lecture 7. Summary (pdf)
  • Midterm (April 23, in-class 10:30~12:30)
  • Lecture 8. Midterm review, Recurrent Neural Network I (pdf)
    • V8. CNN with TF (video)
    • HW2 is out!
  • Lecture 9. Recurrent Neural Network II
  • Lecture 10. TF 2.0 (dnn_tf1, dnn_tf2 / cnn_tf1, cnn_tf2 / cnn_tf2_logging, tf2_tensorboard)
    • V10. Activation functions 1 (video, pdf)
    • HW3 is out!
  • Lecture 11. Generative Adversarial Network (pdf)
  • Lecture 12. Word2Vec (pdf)
  • Lecture 13. Summary (pdf)
    • V13. Dropout & batch-normalization (pdf, video)
  • Final exam (June 11, in class)
    • Content: Lectures 8~13 (including video lectures)
    • Room assignment
      • Room 1: Cluster 507
      • Room 2: Cluster 509


Others

  • Tensorflow Dev Summit 2019 (link)