Hi - I assume the Part 1 will be a top-down approach, which is more like an intro to Deep Learning, like in 2018 and 2018. My question is: would any of the more ‘advanced’ stuff be covered anytime soon? For example, GANs, Transformers or Object Detectors? Or is there no plan in integrating those technologies to Fastai V2.
Thanks
Is part2 also covered in the book or is it separate content? Thanks.
From what Jeremy has said, a mix of both. The course covers the first half of the book, the second part would be the rest, with potentially room for other topics like object detection, etc (Jeremy’s words).
In regards to technologies, GAN’s are in there, with some object detection. That being said, those areas (and transformers) are a good place for smart folks to integrate it in (as it’s open source, so more than just Jeremy and Sylvain, they can only do so much 
) For instance we have a HuggingFace port made already (currently an external library)
(Do note: by technologies I do not mean the course itself. It’s unknown, besides the rest of fastbook)
Thanks for the exciting announcement! Is there a target date for the official release of the 2020 course / fastai v2?
No - it’ll be out when it’s ready. Shouldn’t be too long now.
Please don’t do that. (IE @ing admins when there are plenty of other people who have taken the course live and are on these forums). This question has been answered on the other course-v4 thread. FastAI course v4 MOOC
Thank you Jeremy & all admins for the hard work 
The course, the book, the forums, now the Discord chat 
Can’t wait for part 2 
You wrote:
fastai v2 only works with the upcoming 2020 version of the course. It won’t work with any previous version.
I am struggeling a bit with your approach: I found your great video tutorials and repository some weeks ago. It was very easy for me to install the recent version fastai-1.0.61 on a Windows computer and run the notebooks from the “example” folder. Very nice. I like your approach to make coding available to everyone as quickly and easily as possible.
But: I tried to get into the notebooks from your “courses” folder, but these require fastai-0.7. Unfortunately , these require fastai-0.7 and after some research here and there I found that lots of people are struggeling with getting this older fastai-0.7 installed and running, because fastai-1.0.61 is already available. Thanks for leaving the short message [00-DO-NOT-USE-WITH-FASTAI-1.0.x.txt] in the “courses” folder.
Now you are saying, that with fastai-2 the same will happen again:
fastai v2 only works with the upcoming 2020 version of the course. It won’t work with any previous version.
I really find this a bit frustrating, because drifting deeper and deeper in installation routines is exaclty what I don´t like. So sad that the notebooks in the “course” folder don’t work and are useless for me, because they require fastai-0.7. So sad, that the current notebooks seem not to work in near future once fastai-2 is out there.
Why aren’t these notebooks “backward” compatible? And if a company decides to use your fastai-library for production: would they have to change also their current notebooks each time when there is a new fastai-release?
Its true, you’ll have to install the new library version if you want to use it. But that doesn’t mean you cant use the older version. Only the new course notebooks require fastaiv2. If you want to run older course notebooks, you can run them using v1. The same applies to companies which have developed code using an older version (including v1). That code will still work. fastaiv1 will be shifted to another repository and fastaiv2 will now be known as fastai! Only if they want to use the functionalities of v2, would they require to rewrite their code!
But how can I get these older notebooks from the “courses” folder running? I already have fastai-1.0.61 installed on my own Windows computer, which was very easy (I have Anaconda and Jupyter installed already). But getting the older fastai-0.7 installed on my Windows computer seems not to be easy! With this how-to I run into errors. And also further research on how to install an older fastai-0.7 version on my computer were frightening because of lots of users, who didn’t manage it either.
There are many ways to do this, and many threads that discuss this. The most efficient way i know of is to create a separate conda environment for installing v0.7(that way it wont clash with v1). Though yes, its very cumbersome and frustrating.
I am not sure if I want to follow this path on separating different fastai-releases in different environments. And I see the same problem approaching with fastai-2. 
I am not sure if “fastai” is the right choice for me as these installation routines take a lot of time.
The version might matter, for sure; But applying the concepts and modules is barely done the same way., despite changes in syntax and modules denomination.
assume the Part 1 will be a top-down approach, which is more like an intro to Deep Learning,
Just a hint from my side: I think this community should take their time and update the notebooks of the first course (fastai-0.7) to make these notbooks work for all fastai-releases, also in the fastai-2 version. To me this would make more sense. Imagine if it wouldn’t be possible to open a Word-Documents (.doc) as-of 1998 in my current 2020 Word environment (.docx): what would the people say?
As I am new to all this I still have the choice to move to other deep-learning frameworks, which are less “expensive” concerning frequently changing installation routines.
Updates to the API are never backwards compatible (1:1 without converting it over). What you describe already exists. In the course/ of fastai2 is every notebook from course-v3 updated. (The part 1). There are numerous tutorial notebooks available to help with migration too. No backwards compatibility is applicable here because it’s an ever evolving framework furthering Jeremy’s vision of what he wants this library to be. Read the docs, read the tutorials, and learn the new API if you want. Otherwise Paperspace etc has docker containers (and more exist elsewhere) with proper builds for the older versions of fastai. Also, DL frameworks are not “Word”. They serve two very different purposes. Word has a need to keep track and support outdated work, in this case, that causes so much overhead it’s not worth it. Not to mention: Word - Huge team working on things. Fastai - open source, worked on by generous folks and only Sylvain (who is not with the project anymore) and Jeremy. That’s 1 person (and some very nice people).
TL;DR: Backwards compatibility is extremely expensive and unreasonable for a library with a small amount of devs (dev). fastai is a passion project, and not stemming from a big mega-corp like Microsoft. There are many resources (I made WWF2 to do something similar) including tutorial notebooks and example notebooks, and the entirety of course-v3 on fastai’s repo. Docker is your friend if you are concerned about versioning.
I’d like to add, why I feel its just not a feasible idea to simply change the code from older notebooks to make it compatible with the new version of fastai is because the aim of fastai is not just give tools, but let everyone use the latest ideas in the broad domain of Deep Learning. Deep Learning from 2017 should not be used in 2020. That’s simply the idea behind fastai. Couldn’t have said it better, @muellerzr. Its a passion project, not necessarily intended to provide tools for mega corporations!
@muellerzr @PalaashAgrawal
ok, thanks a lot for the explanation. This makes it a bit clearer to me: a “passionate” project with only a few developers is a good explanation. I understand.
Would this repository have the v1-compatible course notebooks?
https://nbviewer.jupyter.org/github/fastai/course-v3/tree/master/nbs/
Yes it does. A quick TLDR:
Fastai V1 - course-v3
Fastai V2 - course-v4
@andreas.traut note that in every course we mention many times that we strongly recommend not using your own PC, and especially not using Windows. Instead, use one of the supported and documented platforms, which have everything set up ready to go for you.