I do not think putting this information on your resume will be helpful in any way. Even college degrees are secondary at best when it comes to hiring decisions.
But there are many ways you can stand out in the eyes of prospective employers - you can read how students from the previous cohort went about this, a lot of good information floating around on the boards / Twitter.
Above all, @jeremy and @rachel share great advice on this and on learning in general - this is of outstanding value imho, so I am quite sure we can look forward to this happening this session as well
BTW if you are eager to get started, here is a great post by @rachel.
Also, you might want to hop on Twitter as both jeremy and rachel share a lot of information that is very relevant to your question.
Ok, well, this reply came out longer than I anticipated, but I am super excited for the course Super excited this is happening and very happy to be here!
Fast.ai is getting a lot of traction among people working around/in AI, Jeremy and Rachel are working hard to spread the word.
So while it’s not on-par with a recent degree in ML from your country’s top universities, it will
(1) show that you invest time and effort into learning new skills,
(2) add some keywords that recruiters use on their wide searches (tensorflow, pytorch, computer vision, NLP, etc.), and
(3) potentially serve as a conversation opener with people who heard about Fast.ai.
The lessons will be available online a day or two after they are presented - although it is of course important that you are there for the live presentation, since the ability to participate live is the whole point of the program!
I agree with @radek’s response re getting noticed by employers. The important thing is the portfolio you build during, and after, the course.
It looks like it’s AWS. But i am sure you could do this in Floydhub or your own GPU if you have them already. But if we get AWS credits, I don’t see any reason to choose any other Cloud provider for the course work. That way you could focus on learning to do Deep Learning rather than the setup process. The Saturday workshop will most likely clarify things up.
Hi All. Many thanks to Jeremy and Rachel for Part 1 and Part 2 and now reborn Part 1 in Pytorch and International Fellowship program. I was wondering is there a need to spend some time working with Pytorch before the course begin? If yes maybe there are some recommended materials besides:
Can you please share the link to attend live sessions, i am unable to find it along with the schedule of lectures, so that i can attend all live sessions.
We will provide a URL for the stream and forum thread just before the course starts. This will be provided through a private forum category that we will provide access to in a couple of weeks, once you provide your details as discussed below.
and
We will be providing a link to the live stream on the forum closer to the start time.
So we need to keep a check on this forum before the scheduled session begins.
I was trying to learn Tensorflow and high level API’s Keras/TFLearn since last few weeks, now since this course will use Pytorch, is it advisable to keep working through both deep learning frameworks - Tensorflow and Pytorch ?
I was trying to learn Tensorflow and high level API’s Keras/TFLearn since last few weeks, now since this course will use Pytorch, is it advisable to keep working through both deep learning frameworks - Tensorflow and Pytorch ?
My personal experience has been that without applying the knowledge, you will forget the usage of these frameworks. And it is confusing to use multiple of them at learning stage. If you are applying your TF learning in hands-on projects, if should be okay to juggle with both.
I will be sticking with PyTorch along with fast.ai for sometime now. Have heard good things about it’s design, from practitioners.
As @anandsaha said without proper knowledge and understanding of theory knowing many frameworks wont help. Its always better to start coding from scratch (Using numpy) for trivial problems and then you can always re-implement them in any framework of your choice. To understand the strengths and weakness of different framework the following would help!
It’s good to implement from scratch (and we will) but I find it best to first learn to train good networks using existing tools. This is the approach we’ll use in the course.