Yes it’ll be taught in person from Mar 19, and online middle of the year.
I think getting a GTX1080ti is a good idea if you can afford it.
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Happy new year!
Thanks a lot!
I will share this great news via social media. 
Happy new year!
I will switch to V2 directly since I just finished lesson 2 in v1
AW YISS!! so excited! 
oh wow. Not sure how I missed this announcement over the last couple of weeks.
Recently completed with the Brisbane cohort doing the course, we will likely be starting part 2 as a group in the next month.
Will there be an update of part 2 in the near future?
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Yes part 2 starts in person March 19. Will be online a couple of months after completion.
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Ths is great.Thanks for making these courses available.
Hi Jeremy, I am almost certainly more of a beginner than most to this topic. I am working on creating a server from home as I have a GeForce GTX 970 GPU.
I have installed Ubuntu to dual-boot with windows but I am struggling with the paperspace setup script. When I run the
"curl http://files.fast.ai/setup/paperspace | bash" command, it returns with
"rm: cannot remove ‘/etc/apt/apt.conf.d/.’: no such file or directory.
When I just run the script by pasting each line one by one, when I get to,
"sudo rm /etc/apt/apt.conf.d/."
the terminal just closes. Any idea of what could be going on or how to fix it? Thanks.
Hey Seth, in case you haven’t yet, I’d recommend either (a) familiarizing yourself with the very basics of what these setup commands do if you want to run your own server. Or (b) skip that, and go with the paperspace setup, to jump straight into the course.
If you prefer to or must go with (a), does the directory the script is trying to remove exist on your system?
What happens when you skip running that one line, and executing the next commands?
Linux Basics: https://www.digitalocean.com/community/tutorials/an-introduction-to-linux-basics
we had a group complete part 1 in Brisbane just before Christmas. Would be great if we can access the updated part 2 materials while you are teaching in person.
A few are/will-be starting on the current version of part 2. Building a solid group here.
Hi Jeremy
I have the same issue to setup paperspace script “curl http://files.fast.ai/setup/paperspace2 | bash” command in my local computer, not paperspace cloud. Actually, in apt.conf.d, I do not have any files like ‘.’ to delete. In this case, this command maybe has to be skipped!
If I am right, please confirm.
@Seperthar
if you don’t have any files in that folder it is OK, just comment out the command.
But even if you don’t comment out the command script should work fine.
Here’s a first draft of the Video Timelines for Part 1 v2:
I followed last year both part 1 and 2 MOOCs and I am all-in since last year about deep learning (MILA on site course, Kaggle, Coursera, CS231n, books, tons of papers, …).
The top-bottom approach used in the fast.ai course is unique and definitely caught my attention from the beginning in 2016. It was the only viable option for me back then to start learning about this incredible subject. Thanks again by the way to @jeremy and @rachel. Unfortunately, I had not enough time to register to P1 V2 as an international fellow even if I wanted to follow it.
I followed lesson 1 and 2 from part 2 videos and my first opinion coming out is that you should rebrand your course fast(er).ai instead of fast.ai ! With some borrowed reference to fast(er) R-CNN 
Seriously, even if I didn’t setup explicitely on AWS, Paperspace or Crestle, the initial working setup looks faster than last year even if that was already fast.
The fast.ai library from the top view looks pretty clean and allow an even higher level API compared to the previous teaching wrap over keras used in V1.
On the technical side, I’ll remember some nice well implemented ideas from lesson 2 : learning rate finder, differential learning rate, and a personal favorite, progressive learning from lower to higher resolution to overcome overfitting at high res. Very clever ideas. I can’t wait to try this last trick on medical images …
Kudos for part 2 ! And you got my vote for rebranding to fast(er).ai ! Too bad the url is already taken …
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Where can we get assignments and readings for part1 v2,
like it was for part1 v1 (http://wiki.fast.ai/index.php/Lesson_2 )
I’m pretty sure that tasks are changed just a little bit from v1 to v2,
but I assume that readings are extended a lot.
slack channel?
Extra Knowledge is never Harmful…
I am now planning to do part1v1…
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Hi,
I completed Part 1 v1, and started Part 2. I didnt know that there is a v2 of Part 1. Now should I continue watching Part 2, and come back to Part 1 v2 later or should I start doing Part 1 v2 first and then do the Part 2? What is the recommended way?
( I am familiar with most of the basic concepts already, completed 4 out of 5 courses in Coursera Andrew NG course. But I am not familiar with PyTorch yet)
Jamsheer
I am so super excited about finally grokking GRUs thx to the lesson 6 lecture that I wrote a tweet with a link to lesson 6 video… But several seconds after pressing the tweet button I remembered that Jeremy asked us not to share the links! And I take the tweet down.
I then checked Jeremy’s profile and found that he already tweeted out a link to one of the videos. So I tweet again. But then I realize that maybe that is not such a great idea still as I bet the request from Jeremy still holds to not publish this outside a close group of friends / work so I take the tweet down again
@jeremy - I am very sorry for not thinking this through. The tweet existed for such a short period I am hoping it didn’t do any harm. Will refrain from posting links to this till the official public launch!
And I better get back to studying those GRUs / LSTMs - once you start grokking something that seemed initially impenetrable you feel unstoppable And the key ingredient to understanding this were the couple of minutes of video where Jeremy explains the GRU diagram.
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Hi everyone,
thanks a lot to Jeremy for the great course, it’s helping me a lot.
I tried to use the ideas from the Lesson 1 Notebook to participate in the IEEE Camera Model Identification Challenge at Kaggle. I get a validation accuracy around 50% which I think is not too bad. However, when I make and submit predictions on the test set, I seem to produce random noise and get only around 10% at the leaderboard.
Does anyone have any idea what I am doing wrong?
Thanks a lot!!