Deep Learning has been driving the progress of AI in the past decade and has found versatile applications in many products and everyday life. Examples include recommendation systems for online videos, automatic language translation, smart home assistants, and autonomous driving vehicles. This course will introduce general principles, methods, network architectures, and applications of Deep Learning. We cover neural network architectures including convolutional neural networks, recurrent neural networks, Transformer, and graph neural networks. We will cover techniques for designing loss, training, and inference methods. We focus on both the principles, analytical skills and implementation practice. This course is suitable for undergraduate students and graduate students who want to pursue a career in AI or do research in deep learning.
Prerequisite courses: CMPSC 130B; MATH 3B; MATH 6A; PSTAT 120A and 120B.
Students need to grasp knowledge in Linear algebra, Calculus, Probability and Statistics, basic algorithms, and significant experience in computer programming (python, C++, or Java).
Note: apart from the recommended prerequisites above, GOLD does not have enforced prerequisites or enforced restrictions on this course.