Basic information:
Course description
This course aims at covering the basics of modern deep neural networks. In specific, the first part will introduce the fundamental concepts in neural networks including network architecture, activation function, loss, optimization, etc. Then, the second part will describe specific types of different deep neural networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and attention-based Transformer, as well as their applications in computer vision and natural language processing. In the final part we will briefly discuss some recent advanced topics in deep learning including graph neural networks, unsupervised representation learning, deep reinforcement learning, generative adversarial networks (GANs), etc. In this course, the hands-on practice of implementing deep learning algorithms (in Python) will be provided via homeworks and course project.
Textbooks
We will have required readings from the following textbook:
Announcements
Schedule and class notes (being updated)
| Date | Lecture | Reading | Note |
| Week 1 1/13 |
Introduction | - | - |
| 1/15 | Machine learning overview | Deep Learning Ch 1-5 | - |