You can use this category to discuss the Part 1 (2022) Deep Learning course, Practical Deep Learning for Coders.
Great to see fast.ai restarting again.
Are there any considerations to expand fast.ai into computational causality considering the mind bending progress in the field?
Everything in deep learning so far has been correlation on steroids so I wonder if it’s time to make “causality uncool again”?
Anyway, thank you so much for all the great work happening behind the scenes to make all this possible.
Wow, great to see this course restarting. And I feel really thankful to get to be a part of it. Thanks Jeremy and Fast.ai community !
To be honest, when Jeremy first mentioned this on Twitter, I was not expecting to get invited to this edition. The past years have been pretty tiring, and unfortunately I haven’t really contributed/gotten involved much during them.
But, in the recent months I’ve been itching to get back to it, and this invite couldn’t have arrived at a better time for me. Looking forward to follow along the course and join along the conversation here.
Wow! Great to see fast.ai is back again!
Thank you for adding me, I am really looking forward to the next iteration of the course!
great to be back!
im so excited for this new course!
I was really pleased to see the invite in my mail this morning! Good to see some familiar names too!
Thank you Jeremy for the invite. Super excited to have this opportunity to work with Fast AI Friends again. Also, love that we have a short break after week 5 so we can work on projects and everyone can catch up with the help of these forums.
Thanks for the invite! I hope to get access to the lecture streams if possible. I also like the move in the direction of naming this version of the course differently as indicated by the naming of this thread. I have always felt that newcomers are sometimes confused as to which version to start their journey with, and the confusion usually stems from which version to use (v1 or v3 or v4?) .
I would think that something that includes the part number and the year right in the name of the course is a good convention. For my own reference, when I take notes I always use the template on top of my index cards to identify a lecture:
For example I use FA22-P1-L1 to refer to:
FA - Fast AI
22 - year of the lectures
P1 - part of the course (part 1 vs part 2)
L1 - Lecture 1
00:32:33 – I also append optional youtube video timestamp on my index card notes.
Pleased to get the invite! Looking forward to getting more insights into the latest advancements in Deep Learning.
Thank you for the invite! Very excited to participate!
Thanks for the invite. I also hope it’s a new/updated course and not the same as the one from 2020.
Anyway, looking forward to it!
Thank you for the invite. I hope your family are enjoying the Australian life.
Super excited about v5! I’m looking forward to learning more about how can we watch the lessons and if we need to sign up for the paid version offered through the University of Queensland.
Hello. Great to see you again. When does start the course?
Very excited to participate
Now that I know this means I can participate in the upcoming course - !!
Thanks for the invite Jeremy! Really looking forward to taking part in the new cohort and can’t wait for the updated content i.e Transformers !
Thanks for the invite. Excited and honored.