Deep Learning

4-None

Deep Learning has been driving the progress of AI in the past decade and has found versatile applications in many products and everyday life. This course will introduce general principles, methods, network architectures, and applications of Deep Learning. Covers neural network architectures including convolutional neural networks, recurrent neural networks, Transformer, and graph neural networks. Covers techniques for designing loss, training, and inference methods. Focus on both the principles, analytical skills, and implementation practice. Suitable for undergraduate and graduate students who want to pursue a career in AI or conduct research in deep learning.