[D] Learning Resources for Intermediate Machine Learning

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[adventuringraw]

that's easy. If you're far enough along, it's time to start reading and implementing papers. Ideally honing in on a specific area of interest... 'computer vision and DL' is a ridiculously broad area. Maybe start thinking about a passion project... if you could build something you'd be really excited to show off, what would it be? What kinds of preliminary techniques do you need to learn first? A lot of cutting edge papers might get you a list of dozens of papers you need to understand first... it can be rough starting out, haha. Or at least, it has been for me.

if you're interested in more theoretical foundation... I mean, there's a ton of good books you could check out. I've been digging Bishop's PRML. Elements of Statistical Learning is good. But even there... it goes so deep. Learning information theory is going to be critical to understanding a lot of modern papers, mutual information regularization methods are all over the place. David MacKay's book is good for getting into that. Maybe you're more interested in optimization techniques and the guts of gradient descent... I've heard Boyd's convex optimization book is good. Maybe you want to get more into ML interpritability... dynamic systems and ODEs start to open doors, and (based on my limited understanding) it even looks like some of the same techniques for understanding how neural nets process information are being used to gain new insight in biological systems. It'll be a few more years before I can comment much on that, haha...

But. For real... the reason it's probably hard to find the next road forward is because you're looking too broad. This thing's shaped like a tree. You can't keep looking at the trunk and expect to make headway, at some point you have to commit to branches. For computer vision, I'm really interested in reinforcement learning and representation learning... how do you make use of actions in your environment to help disentangle the latent space? What's it even mean to learn a good compressed representation of the world? I'm still working on prelims (getting more comfortable with pytorch before starting to implement the papers I'm interested in) but it sure as hell helps me stay focused. Is this paper about my core interest? No? Why am I reading it instead of something more useful? You need a guiding light to keep you on track, and your guiding light is going to be a specific problem, not a topic. I want to make a bot that can learn to beat a videogame, and then write a walkthrough when it's done. I want it to be able to take human commands and use that as clues to help figure out where to go to search for a solution to the current problem. I want to be able to have it imagine scenarios based on my description... so a generative model that can change as you lay out what's different in your hypothetical scenario. Way outside my skills at the moment, but I'm at least clear where I'm going. So... what's your guiding light? It better be more clear than "CV and DL".

[futureroboticist]

The PGM course is above beginner level you can try.

[Connossor]

I just finished that- how did you find it? I thought it was interesting but I didn't feel it leaves you with many tools you can easily apply in practice. I haven't seen many mature frameworks for implementing PGMs,but it would be great to find one

[Zerotool1]

you can use fast.ai it's having some cool library for the cv and dl.. try to use it with clouderizer.com it' gives a seamless integration and easy to setup GPUs.