About the course

Deep Learning has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection and language understanding tasks like summarization, text generation and reasoning. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art systems.

This course is a deep dive into the details of deep learning algorithms, architectures, tasks, metrics, with a focus on learning end-to-end models. We will begin by grounding deep learning advancements particularly for the task of image classification; later, we will generalize these ideas to many other tasks. During the 10-week course, students will 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 will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.

Important Links

Canvas: https://canvas.uw.edu/courses/1694426

Gradescope: https://www.gradescope.com/courses/687869 (Code: YDZ26P)

EdStem: https://edstem.org/us/courses/50490

Course Staff + Office Hours

Instructors		Teaching Assistants
Ranjay Krishna	Sarah Pratt	Ainaz Eftekhar	Mahtab Bigverdi	Zihan Wang	Xiyang Liu	Tanush Yadav
Hours: Tue,	Hours: Fri,	Hours: Thu,	Hours: Mon,	Hours: Wed,	Hours: Wed,	Hours: Thu,
9:00-10:00am	10:30am-12:30pm	2:00pm - 4:00pm	9:00-11:00am	10:00am-12:00pm	11:00am-1:00pm	11:30am-1:30pm
CSE2 304	CSE2 376	CSE2 276	Allen 3rd floor TA breakout	CSE2 374	CSE2 374	CSE2 153
ranjay@cs. washington.edu	spratt3@cs. washington.edu	ainazeft@cs. washington.edu	mahtab@cs. washington.edu	avinwang@cs. washington.edu	xiyangl@cs. washington.edu	tanush@cs. washington.edu

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Prerequisites

Linear Algebra, Calculus and Statistics. While it is recommended to have some prior background in Machine Learning, the necessary fundamentals will be covered as part of this class.

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Course format

The class format will be a combination of lectures, 5 assignments, 5 in-class quizzes, and a course project.