Gradescope (Entry Code: R5KJXK), Ed Board, Canvas, Exam Archive, Project Archive
This course is a deep dive into the details of deep learning algorithms, architectures, and tasks, with a focus on end-to-end models. We begin by grounding deep learning advancements particularly for the task of image classification; later, we generalize these ideas to many other tasks. During the 10-week course, students learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in deep learning. Additionally, the final assignment provides the opportunity to train and apply multi-million parameter networks on student-chosen real-world vision problems. Through multiple hands-on assignments and the final course project, students acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.
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.washington.edu |
.washington.edu |
.washington.edu |
.washington.edu |
.washington.edu |
.washington.edu |
.washington.edu |
.washington.edu |
.washington.edu |
Calculus (Math 126), Linear Algebra (Math 208), and Probability (CSE 312 or Math 394).
CSE 446 is NOT a prerequisite. The neccessary fundamentals of machine learning will be covered in this class.
This class consists of lectures & recitations, 5 assignments, 1 in-class exam, and a course project.