Description

Deep Networks have revolutionized computer vision, language technology, robotics and control. They have a growing impact in many other areas of science and engineering, and increasingly, on commerce and society. They do not however, follow any currently known compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This is a fancy way of saying “we don’t understand this stuff nearly well enough, but we have no choice but to muddle through anyway.” This course attempts to cover that ground and show you how to muddle through even as we aspire to do more. That said, we will be leveraging the tentative understanding that we have gained in the past few years.

Lecture reference materials

Lectures are webcast by the department and recordings will be posted to a youtube playlist. You must be logged into your @berkeley.edu account to access the videos. Lectures will have a substantial amount (if not all) of the content covered on the whiteboard (not on presentation slides). You are expected to take handwritten notes and use those to study.

Because Deep Learning is rapidly evolving field, the material covered in this course can change substantially from semester to semester. If you are interested in materials from previous iterations of this course, please see here: [Sp21] [Sp22] [Fa22] [Sp23]

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