深度學習與實務

(1) To understand the maths of deep learning techniques (2) To familiarize with deep learning tools, such as PyTorch, Tensor Flow, etc. (3) To understand the latest developments and applications of deep learning techniques (4) To develop practical working systems

Linear Algebra, Probability Theory, Machine Learning (suggested) Rn56-EC114 (for lectures) Tabc-EC114 (for Lab) NOTE: (1) The first lecture will begin on Feb. 25 (Rn56-EC114). (2) If you want to enroll in this course, you need to be present on Feb. 25 (Rn56-EC114) and submit your enrollment form in person (if you have not yet been enrolled successfully). (3) Be advised that if we have more students taking this course than we could afford, your final enrollment will be subject to review by all the instructors. (4) More details will be announced during the first lecture. Make sure that you don't miss it.

Labs (done individually) 40%, Paper presentention (done in groups of 2 members) 20% Final project (done in groups of 2 members) 20% Final exam 20%

1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st Ed., MIT Press, Dec. 2016 2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Nov. 2017