11-785 Introduction to Deep Learning
Location: Pittsburgh
Units: 12
Semester Offered: Fall, Spring
Course description
“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market.
In this course, we will learn about the basics of deep neural networks and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.
If you are only interested in the lectures, you can watch them on the YouTube channel.
See the original course description for the most recent information.
Prerequisites
- We will be using Numpy and PyTorch in this class, so you will need to be able to program in python3.
- You will need familiarity with basic calculus (differentiation, chain rule), linear algebra, and basic probability.