Deep Learning Foundations Signup, Open Source Scholarships, & More

Signups might not be available any more. If you are able to do a late signup, note that you may miss doing the first lesson live (although you can watch the recording.)

Signups now open

Last week we announced our new course, From Deep Learning Foundations to Stable Diffusion, which is part 2 of our Practical Deep Learning for Coders series. Part 1 of the course, which is available now is a free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems. Part 2 will be an in-depth course that starts from the foundations—implementing and GPU-optimising matrix multiplications and initialisations—and goes all the way through to implementing the astounding Stable Diffusion algorithm (and many other papers along the way).

Signups for the new part 2 course are now available. Lessons will run weekly from Oct 11th on Tuesdays 6:00 – 8:00PM AEST (UTC/GMT +10 – note that that’s Mondays US time). You can either watch the lesson live (and ask questions and chat live to other participants), or you can watch the recording which is available immediately after the lesson. All participants get access to the course forum, where there will be a bustling community of students like you, along with experienced alumni helping answer your questions.

Note that, although 8 lessons are listed on the signup page, this is just our best guess, since we’ve never run this course before and we don’t know exactly how long it will take. We also don’t know what new papers might come out during the course which we may want to cover. It will certainly take at least 7 lessons, but it might take 8 or more lessons. If you can’t commit to attending all of them live, that’s no problem – you can always watch the recordings later at any time. (Oh and also, ignore the “Learning Outcomes” section of the signup page – it looks like the university just copied that from the part 1 course!)

To get the most out of this course, you should be a reasonably confident deep learning practitioner. If you’ve finished part 1 of our course then you’ll be ready! (If you don’t have much background in deep learning, but are a confident coder and have some familiarity with other machine learning approaches, you should be OK to do part 2, given some patience and tenacity—I’ll provide an introduction to every topic we encounter on the way.)

Scholarships

We’ll provide free access to the live course to the following groups upon request (to get access to the course if you qualify, please complete this form:

Open source: Core contributor to an open source project with 50+ stars on github
Study group organisers: Ran a public fast.ai study group with 6+ members
Academics using fast.ai: Published a scientific paper with 10+ citations that cites and fast.ai project
Open source using fast.ai: Created a project with 5+ stars on github that uses and references any fast.ai project
Provided a transcription or translation of a previous lesson.

We’ll also provide free access to the following groups, which have already been automatically added to the course and should have received a notification (to check, login to the forums and see if you see “Part 2 2022”):

Forum expert: Anyone who has posts on forums.fast.ai receiving a total of 45 or more “like”s in the last 2 years, or 90 or more at any time
Diversity scholars: All diversity scholars from previous courses