一、課程說明(Course Description)
This is introductory course of deep learning and its applications to
biomedical imaging.
二、指定用書(Text Books)
Lecture Notes and Papers.
三、參考書籍(References)
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
(http://www.deeplearningbook.org/)
2. C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
(https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book)
3. Keras. https://keras.io/
4. PyTorch. https://pytorch.org/
5. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach,
4-th Ed.,
Pearson, 2020.
6. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An
Introduction, MIT
Press, 2018.
7. Haykin, Simon, Neural Networks and Learning Machines, 3rd. Ed., Pearson, 2016.
四、教學方式(Teaching Method)
Lecture
五、教學進度(Syllabus)
1. Introduction, Python, Keras (PyTorch), Anaconda, Google Colab.
2. Perceptron and Neural Networks.
3. Convolutional Neural Networks (CNNs).
4. Descent Optimization Algorithms, Normalzations.
5. Notable CNNs: LeNet, AlexNet, VGG 16 + 19, GoogleNet
6. object Detection: R-CNN
7. Residual CNN
8. Autoencoder, Generative Adversarial Networks
9. Denoising
10. Segmentation Using U-NET
11. Transformers (Attention Is All You Need)
六、成績考核(Evaluation)
1. Homeworks + Exams+ Projects 80%
2. Presentation 20%
七、可連結之網頁位址
https://eeclass.nthu.edu.tw/course/bulletin/12001