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IMPORTANT(2026-01-09) This is a WORK IN PROGRESS, and not finalized.

Course Description

From the UC Berkeley course catalog:

This course is an introduction to deep learning (also known as “deep neural networks”), and will cover the fundamental techniques that power deep learning algorithms, as well as exploring the intuitions and various “rules of thumb” behind successful deep learning methods. Topics include: neural network architectures, backpropagation, convolutional neural networks, sequence models (such as the transformer model), applications to computer vision and natural language processing, and more.

Important: In this course, we will use Edstem to post announcements and important information.

It is the student’s responsibility to actively monitor the Ed for any important announcements.

Useful course links:

• bCourse: link
• Ed: link
• Gradescope: link

Textbooks

We will be using the following textbooks, all fortunately freely available online:

• "Deep Learning: Foundations & Concepts" by Christopher Bishop & Hugh Bishop. The free-to-use online version is at Bishop Book. Further, UC Berkeley students can access the PDF version via this link (CalNet login required): PDF.
• "Deep Learning" by Ian Goodfellow and Yoshua Bengio and Aaron Courville. Free online link is here.
• (optional) Dive into Deep Learning D2LAI is an excellent interactive online textbook and set of resources for Deep Learning ! (a PDF version of the entire book is also available online)

Lectures

Lectures are Tuesdays and Thursdays, 3:30PM - 5PM, online via Zoom. Lecture slides are provided via this website, and lecture videos are provided via the bCourses “Media Gallery”. Students are responsible for all lecture content.

Lecture Slides

Discussion Sections

The discussion sections will not cover new material, but rather will give you additional practice solving problems. You can attend any discussion section you like. However, if there are less crowded sections that fit your schedule, those offer more opportunities for you to interact with your TA.

Section Notes

Homeworks

All homeworks are graded for accuracy. See the course syllabus for info about collaboration, slip day, late policy.

Homeworks