What you need is determination. There's no substitute for this.
If you have that, there are all kinds of resources. Here are a few...
Resource 1: a community. We've set up a discord server for AI dev. It has 360 users. At any given time, around ~50 people are online, of which ~10 are skilled devs. Come join! https://discordapp.com/invite/x52Xz3y
Resource 2: Find something fun for you, and pursue that. I like generative AI, so for me that's been GPT-2 and StyleGAN. Gwern has some lovely tutorial-type articles on both.
The reason to follow them is, whenever you see something that seems interesting or fun, tweet at them and say so! Ask questions. Ask how to get started. Everyone is shockingly nice and helpful. My theory is, the software is so crude and often hard to use, that we all like to celebrate together whenever one of us gets it working, and we're happy to share that knowledge however we can. (Twitter is a bit chaotic right now due to world events, but I imagine it might return to normal within a couple weeks.)
And yes, you're right about fast.ai and other courses. You can go that route if you like it. I found it more exciting to dive into the deep end, though, and try to tinker with stuff.
But, have you achieved something singificant in the context of deep learning, as per your earlier comment? There's no information about that in your profile and a cursory glance at ddg and google results for "shawn presser" doesn't turn up anything very relevant.
So, I have to ask: without having studied CS, what contributions have you made in deep learning that are widely recognised?
I hope you agree this is a reasonable question to ask, and that you are not offended by it. Otherwise, I apologise because it's not my intention to offend you.
To be honest, and again without having an intention to be harsh, but those are not what I'd call "widely recognised contributions to deep learning". They're mainly articles in the lay press and a honourable mention in a DeepMind blog post. They certainly sound like contributions to Shawn Presser's reputation, but "contributions to deep learning"?
To clarify, what I was hoping to see is, at best, an article published at a reputable venue for AI research, a conference or a journal, or at a minimum an arxiv article that at least looks like it was meant to be submitted to a conference or journal. And at worst, a software tool that can be used in deep learning research. But it seems to me that your achievements are mainly having fun with and in one case finding an interesting use for tools that are already available.
Again, I'm not trying to be harsh, neither do I want to say that all this is not worth the trouble. But it should not be held up as an example of what people can achieve without studying CS. Because, I think you'll agree, they are kind of underwhelming when compared to what people routinely achieve who have studied CS.
I _super_ appreciate this, thank you for sharing. I've had to deal with a lot of gate-keeping in my career, so its great to interact with people who are so open to invite people in. We could use more with such a positive attitude.
What you need is determination. There's no substitute for this.
If you have that, there are all kinds of resources. Here are a few...
Resource 1: a community. We've set up a discord server for AI dev. It has 360 users. At any given time, around ~50 people are online, of which ~10 are skilled devs. Come join! https://discordapp.com/invite/x52Xz3y
Resource 2: Find something fun for you, and pursue that. I like generative AI, so for me that's been GPT-2 and StyleGAN. Gwern has some lovely tutorial-type articles on both.
GPT-2: https://www.gwern.net/GPT-2
StyleGAN: https://www.gwern.net/Faces
Peter Baylies' StyleGAN tutorial notebook is a hands-on resource. This was actually how I started, nearly a year ago.
github: https://github.com/pbaylies/stylegan-encoder
notebook: https://colab.research.google.com/drive/179SPYbBC8pKDxVRjZep...
(The original notebook was broken; this is a copy I've updated with some fixes.)
Lastly, follow a bunch of AI people on twitter. Here are a few to get you started: https://twitter.com/i/lists/1160386581850730496/members
The reason to follow them is, whenever you see something that seems interesting or fun, tweet at them and say so! Ask questions. Ask how to get started. Everyone is shockingly nice and helpful. My theory is, the software is so crude and often hard to use, that we all like to celebrate together whenever one of us gets it working, and we're happy to share that knowledge however we can. (Twitter is a bit chaotic right now due to world events, but I imagine it might return to normal within a couple weeks.)
And yes, you're right about fast.ai and other courses. You can go that route if you like it. I found it more exciting to dive into the deep end, though, and try to tinker with stuff.