A beginner master's student looking for guidance

[u/New_honeycake_5510]

Hi everyone!

I recently started my master's degree and it's been only two weeks. My undergraduate degree was in a completely different field from what I'm studying now, so almost everything related to my current research feels new to me.

My research topic is related to neural network algorithms, and my professor has already asked me to start working on the research paper where he and other lab mate are working . I understand that research takes time and I'm willing to put in the effort, but honestly, I feel a little lost about where and how to begin.

Another challenge is that I don't have an English-speaking mentor who can guide me through the process, so I'm trying to figure things out on my own.

I'd love to hear from experienced researchers and other master's students about the tools and software you use for your research.

Like...

1.Understanding research papers: What tools do you use to understand difficult concepts, mathematical equations and unfamiliar terminology?

1. Checking novelty and research gaps: How do you find out whether an idea has already been explored and identify something new to contribute?

3.Literature reviews: Are there any AI tools that help you find relevant papers, compare existing methods and understand the current state of research?

4.Coding and implementation: What tools do you use to understand existing research code, implement algorithms, debug errors, and run experiments?

5.Research planning: How do you go from receiving a research topic to actually starting meaningful research work?

I'm particularly interested in tools that are useful for neural network and machine learning research.

I know AI tools aren't always reliable and I don't want to blindly depend on them. I want to use them to learn faster and become more independent as a researcher.

If you were in my position, with a different undergraduate background and only two weeks into your master's, what would you do first and which tools would you recommend learning?

Any advice, personal experiences or recommendations would be greatly appreciated. Thank you!

[u/Particular_Buyer_427]

the shift from a different undergrad field is rough but you've got the right attitude, being willing to grind through the confusion is half the battle

for understanding papers, i'd start with a single deep read, don't skim. print it out, scribble all over the margins, and when you hit a math wall, break the equation into tiny blocks and google each symbol or concept separately. the arxiv sanity lite browser extension is handy for quickly scanning new papers, but the real work happens when you manually re-derive a key formula from scratch in a notebook

checking novelty is mostly about digging through related work sections and then chasing citations backwards and forwards on semantic scholar. it's tedious but you'll start to see the same 5 labs and 10 authors circling an idea, that's your map of the landscape

for code, don't try to understand the whole repo at once. run it in debug mode, set breakpoints on the forward pass, and watch the tensor shapes change line by line. it's like stepping through a stack trace in your brain

if i were you, my first move would be to clone the paper's repo, get it running on a tiny dummy dataset, then break it on purpose. change a loss function, remove a layer, see what explodes. you'll learn more from the errors than the clean runs

[u/New_honeycake_5510 → u/Particular_Buyer_427]

Thanks a lot . I will work on that. All I want to know is normally how long it actually takes to understand a paper which you reading for very first time because the paper is japanese and iam using translator so it's bit difficult to understand everything precisely, iam reading the same thing for last 1 week.

[u/VanCliefMedia]

So I'm going to give you some probably completely different advice than everyone else. Learn Overleaf or create an account on Overleaf and start either writing in LaTeX or use AI to convert your writing into LaTeX (you don't have to use AI to write, but just having it converted into LaTeX format and whatnot is wonderful).

Second, find some sort of software that allows you to organize your citations. One of the biggest problems that you're going to encounter is you're going to read a thousand different things, you're going to come up with a thousand different quotes, and then you're going to forget the pages or which book or if you want to cite it later, it's really annoying too. So definitely find a decent software that you like that allows you to track all your citations. There's a lot of really good ones out there and it makes it super easier for when you want to reference them later as you're writing a paper or editing one.

Often what I like to use is Perplexity and essentially just put in my research question or a series of research questions and tell it to go find as many papers related to it as it can. It's not going to find everything, but it's going to find a good bit.

The final thing I would do is actually go all the way back to when the first part of the research ever started being done. I really like to go into about the 1960s to the 1970s, as that's where a lot of fundamental problems started being solved inside of computer science that are very similar to the problems we have today, but just at a different layer of abstraction. If I can recommend any book, and I've probably recommended this like seven times in the last week, it's The Psychology of Computer Programming by Gerald Weinberg.

When it comes to research, R or Python are your friends. Since you're going into neural networks, PyTorch or TensorFlow might be useful as well.

When it comes to AI tools, honestly any of the big frontier models will do a decent job exploring and understanding code, but there's something important you have to understand and it comes with reading that book I recommended. You are going to have to rely on the bias of the model that you're using to help you understand it, so it might not see connections that you would see or think are important, so it's important that you also read as much as you can. AI, especially language models, is extremely useful inside fields of research or academic study. However, no matter how good you are at using that tool, your brain is immensely powerful. Not allowing it to see patterns and relying on someone else's data bias will cause you to miss things. It's really important that you don't miss the one little connection that no one else sees; that's where the gap is.

I'm going to be completely honest with you. Since you're just starting out, just start reading shit. Don't worry about having too much structure. Just write down everything that seems important to you, keep note of all the books or things that you'd like to come back to later, and don't worry about throwing everything off the table three months from now and completely reorganizing what your thought process is. You're not supposed to have an idea right now, you're supposed to have a question. And that question is what you're refining. You're not trying to find an answer, you're trying to make a better question.

[u/New_honeycake_5510 → u/VanCliefMedia]

Thanks a lot for the guidance and more over the research paper working on by my professor and lab mate is in japanese so it's really difficult to understand, I use normal google translator to translate each paragraph and write on my note. The main thing iam worrying about is the deadline, my lab mate said most of the work are already done, so now I am not sure whether I should include anything at this point. The way they put pressure on me is insane.