課程概述 |
1. Mathematical model of neural networks and history of neural networks. 2. Why deep? Universality theorem for shallow/deep neural networks. 3. Optimization algorithms: back-propagation, block-coordinate descent algorithms, un-rectifying algorithms, and target propagation. 4. Classification problem: dimension reduction methods, support vector machines and kernel methods. Un-supervised learning and auto-encoder. 5. Generative Adversarial Network. 6. ResNet, Mobile Net, U-nets, and RNN. 7. Using DNN to solve problems: the inverse problem, the forward inference problem, and the generalization. |