If you are reading this, it means you are part of the 2022 fast.ai course - welcome! The course will cover material from the first half of the book, plus new material (such as the Transformers architecture for natural language processing). You strongly recommend prerequisites of a year of coding experience, and high school mathematics.
There are two groups that will be attending:
People who are part of the official University of Queensland course
Selected members of the fast.ai community that have been selected for the fellowship program
The first lesson will be April 26th, 6pm, Brisbane Australia time zone. Online streaming will be available via YouTube. Recordings will be available immediately for everyone in this group, and will be available later this year to the general public. Click on the following links to see the lesson times in your time zone:
Thank you, @jeremy for giving us so much! I am eagerly looking forward to the course, and especially the dissection of the Transformer architecture that youāll do!
Oh wow, that was a long time since I participated in the course! And many changes happened in the Deep Learning landscape. Would be great to know Jeremyās opinion on the recent trends, like multi-billion parameter LMs and Transformers in general. The fast.ai courses was the first place where I learned about applying transfer knowledge for NLP. So maybe weāll get some great insights this time as well
Correction: I wrote originally āNote that in the US this will be April 25th.ā But thatās wrong. Itās actually first thing in the morning April 26th.
Iām so happy to hear that your courses are staring up again. Theyāve already made an incredible difference in my life. I hope youāll let us know how youāve been doing these last couple of years and how your plans to boot up Deep Learning in Australia are going. Thanks very much for inviting us!
Iām so very happy to be here! Iām looking forward to the course and Iām reading the book again to be better prepared. Beyond the content, Iām also eager to enjoy those practical pearls of advise that are dropped casually during the classes, and to reconnect with this fantastic community. Thank you!
This is the only course I take every time it comes out. Even if it starts from the basics: thereās always something new, Jeremy & Rachel have this independent way of researching thatās just , and this community is the best and most accessible way to get into ML.
I started here years ago and learned ML hand in hand w software dev. Now coming from industry Iām looking forward to seeing whatās new to learn and helping out.
Thanks a lot for the invite, I really appreciate it.
Glad to hear the course is offered again and thanks for the invite. @jeremy your teaching is incredible and fast.ai is generous to offer such learning opportunities for everyone. Eager to learn new material on Transformers from you as I tend to suggest this to one of my PhD student who is trying to develop an AI model to generate Python code from clinical documents.
. And head over here to introduce yourself: Introduction thread.
, and this community is the best and most accessible way to get into ML.