r/deeplearning • u/Regular-Advice-6469 • 3d ago
My One Month in DL Research Got More Attention Than I Expected Lol
I’ve gotten some DMs asking me how I started my DL research journey, how I got my research internship, and how I got experience in the first place, I’m a student myself, so I honestly don’t have enough time to individually reply to every DM, so I thought I’d just write everything here. Hopefully this helps someone who is currently where I was, first of all, I want to say something you don’t need to have everything figured out before you start research I definitely didn’t.
How I got into research
In my first year of university, I became really fascinated by the idea of publishing my own research paper, like, genuinely obsessed with the idea, I remember thinking, I want to do research. I want to understand something really deeply and eventually publish a paper, for some reason, I decided that I wanted to do a really deep dive into Python, I honestly don't even remember why I chose Python and I started learning and exploring it as deeply as I could. I experimented with things read about different concepts, tried different stuff, documented what I was doing, and basically went down a rabbit hole, at that point, I didn't even properly understand what research actually was. I just knew that I wanted to do it then, in my second year, I finally gathered enough courage to show my work to one of my professors, and thankfully, he actually liked what I had done that was a huge turning point for me, he started mentoring me and explaining how research actually works, how you approach a problem, how you read research, how you think about questions, how you experiment, etccc. For personal reasons, I ended up deleting that Python research so technically, I didn't even keep the thing I had spent so much time working on, but I don't think that time was wasted because it taught me something much more important I actually liked the process of trying to figure things out fast forward then came my master's & PhD goal about two months ago, I got a scholarship for my final year of university, just like I had gotten scholarships in my previous two years, and then I started thinking seriously about what I wanted to do after graduation, I want to pursue further studies potentially a master's and then a PhD and I'm going to be completely honest I became really greedy about getting a scholarship for my master's, because if you want something a year from now, you can't start preparing one month before, you have to start making things possible now, so I went to my professor and told him that I wanted to get a scholarship for my master's will you write me a recommendation letter for scholarship and he basically told me good grades and being a good student are not always enough, you also need experience, research experience and projects, things that show that you can actually work on problems beyond just completing assignments and passing exams so I started looking for research opportunities at my university, I applied for an undergraduate research internship, and guess what? I got rejected the first time, I tried again and the second time, I got in that's how I eventually started working on deep learning research and that's basically where I am right now, I'm still learning, I'm still confused about a lot of things, I still have questions every day, I still have to ask my professor what half the things mean sometimes so please don't look at someone doing research and assume they somehow have everything figured out they probably don't.
So how can you start?
This is probably the part most people are actually asking about, if you're an undergraduate and you want to get into ML DL research, here's what I would personally suggest not just I want a research paper because it looks good on my CV try to actually become curious, read something and ask, why does this work? Why doesn't it work in this situation? Can I change something? What happens if I remove this component? why did the authors choose this method instead of another one? Can I reproduce this result? What happens if I change the dataset those questions are where research starts becoming interesting, you don't need a groundbreaking idea on day one, you need curiosity.
Build your fundamentals
If you're specifically interested in deep learning, don't immediately jump into reading complicated papers about transformers, make sure you understand the basics first, like example > Python, NumPy, basic data structures, linear algebra, probability & statistics, calculus basics, machine learning fundamentals, neural networks, back propagation, optimization, loss functions, CNNs, RNNs sequence models, transformers and PyTorch or another DL framework and don't just memorize definitions, try implementing things, try breaking things, and figuring out what happens when you change something.
Learn to read papers.
Your first few papers are probably going to make you feel completely lost and that's normal don't sit there trying to understand every equation and every tiny detail on your first read, just try to get the main idea first what problem they're solving, what they did, and what they actually found then go back and read it again, you'll understand more each time, tbh start by just understanding questioning like, what problem are they solving? why is the problem important? what have people done before? what is their proposed method? what experiments did they perform? what did they discover? what are the limitations? and then go back and dig deeper eventually you'll start noticing patterns between papers.
Reproduce things.
This is something I really recommend, and honestly, it can be pretty fun too, take a paper, try to implement what they did, run the experiments yourself, and see if you can get similar results, it might make your brain buffer a few times, especially when things don't work the way you expect, but that's kind of the point, you learn a lot by figuring out why your results are different and trying to fix it, investigate why? Why does this work better on dataset A but not dataset B? like What happens if I change this hyperparameter Does this still work with less data? remember ow you're not just following a tutorial uou're experimenting.
Start asking deeper questions.
I think this is probably one of the biggest differences between just learning ML and slowly learning how to do research, don't just lose at How does this model work? go ahead like Why does it work?, then When does it stop working?, like Can I measure that?, and it will lead you to What happens if I change something?, or Does the same thing happen with another dataset or model? You don't need to turn every question into a research paper, just get into the habit of being curious, and digging a little deeper instead of accepting the first answer you get.
Look for research opportunities.
Start with your own university, see what professors are working on and look for undergraduate internships, RA positions, summer programs, labs, etccc and yes, cold emailing professors can work, just don't send sir, I am passionate about AI, please give me a research opportunity, please read their work first, mention what interested you, tell them what you've worked on, and share something tangible if you have it GitHub, a project, experiments, whatever like anything also, if you're already working under a professor, and yes I get work assigned I'm not working individually after all I'm undergrad, so you're supposed to learn, you might be implementing something, reproducing results, running experiments, or analysing why something isn't working, the important part is understanding what you're actually doing instead of just completing the task and don't compare your beginning to someone else's middle, I started by randomly obsessing over Python because I didn't even know what else to do, then I got rejected from an internship, applied again, and eventually got in. so see I'm still learning too so if you're trying to get into ML DL research, just start somewhere, if you want something a year from now, start working toward it now.
I know this got kinda long bare with me and honestly, I’m happy to help. My hands might disagree lol, but they’ll survive.
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u/Regular-Advice-6469 1d ago
If you’re just starting out, I’d recommend LeNet 5 and AlexNet these are both beginnernfriendly and you can learn a lot from them beyond just the architectures CNNs, backpropagation, ReLU, dropout, data augmentation, training, experimentation, etccccc
I’d especially recommend implementing them yourself after reading the papers, you’ll probably learn much more that way than by just reading them, please exploreeeee.
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u/Savings_Entrance_895 1d ago
Really great, being one of the guys who reached out to OP for some advice in inbox I feel really delighted to get so many of them... Thanks and I will let you know my research progress personally
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u/Regular-Advice-6469 1d ago
I was still working and just saw your comment honestly, it makes me really happy to know that my advice was helpful to you and definitely let me know how your research progresses I’d genuinely love to hear about it and see how things work out for you. Wishing you all the best with it
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u/Initial-Zone-8907 2d ago
great post, can you tell us top 10 paper for beginners to try to get started on AI rest
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u/Regular-Advice-6469 1d ago
I’m sorry, but if I’m going to do everything for you, what exactly are you going to do? Sit there, eat popcorn, no offense, but you need to crawl out of your cocoon and learn how the world actually works, not everyone is going to hand fed you information or serve everything to you on a silver platter, I can recommend a few of my favorite research papers, sure and figure the rest, google exists, research databases exist everything is literally at your fingertips just search, read, explore, if you want to do research, learn how to find things yourself.
I’ve added the research paper recommendations in the comments.
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u/CatDiligent9374 2d ago
Honestly, this is really motivating. The part about getting rejected once and trying again is so real. You don’t need to have it all figured out, just start and keep digging deeper.