This course provides an introduction to deep learning. Students taking this course will learn the theories, models, algorithms, implementation and recent progress of deep learning, and obtain empirical experience on training deep neural networks. The course starts with machine learning basics and some classical deep models, followed by optimization techniques for training deep neural networks, implementation of large-scale deep learning, multi-task deep learning, transferred deep learning, recurrent neural networks, applications of deep learning to computer vision and speech recognition, and understanding why deep learning works. The students are expected to have some basic background knowledge on calculus, linear algebra, probability, statistics and random process as a prerequisite. The course offered in
Spring 2023 features:
- The latest developments in deep learning, e.g., deep reinforcement learning, GAN, RNN with language models, video analysis and so on.
- Hands on experience with the optimization of deep learning, using popular DL toolkits (for example, PyTorch).
- The final project will walk you through the whole pipeline of doing research: drafting proposal, discussing ideas, conducting experiments, writing report, and sharing your work via the presentation!
