r/MLQuestions

I am a rising senior who has spent my undergrad preparing for a PhD, with the long-term goal of transitioning to industry and founding a startup (specifically focused on world models).

My main concern right now is Intellectual Property. I've read that if a company or product is tied to university research or resources, the institution can claim around 50%+ ownership. Giving up that much equity is a big concern for me.

I genuinely want to do a PhD for the learning experience and to build the credibility and technical foundation necessary to attract investors. I've worked hard to become a competitive applicant: a 3.9 GPA, multiple graduate courses, an NSF-funded REU, and two separate paid university research positions in math and CS. I also do not want to pay out of pocket for a Master's degree.

Because of my love for research, I kept pushing this IP conflict to the back burner. But now that I am at this point, I am wavering.

How restrictive are university IP policies in practice? Is there a way to safely pursue a PhD without compromising the IP of my future startup? Should I not pursue a PhD? Is Industry research an option even without a PhD? Any advice or shared experiences would be greatly appreciated.

My gpu usage is pretty inconsistent, some weeks I'm running stuff every day and then I wont touch it for two weeks. Probably 15-20 hours a month total if I average it out.

Buying a card sounds good until you realize its just sitting there most of the month doing nothing while losing value. I worked it out roughly, if a card pays for itself in under 3 months of constant use I'd buy it. Around 6 months I'd think about it. Beyond that renting wins and at my usage I'm way past that point.

Right now I'm on RunPod at 99 cents an hour for a 5090. A coworker mentioned finding cheaper options like HyperAI at 35 cents, but I haven't verified that yet. Are there other providers in that price range people have had good experiences with? At my usage level even a small difference per hour adds up though.

Something that has been on my mind lately:

Humans can usually get used to a place and learn fast with just a little bit of experience.

For example a person can figure out rooms, objects, obstacles and how things move around after seeing just a few examples.

Physical AI systems seem to need a huge amount of real-world data, simulation, retraining and coverage of all the edge cases before they work well.

Then small changes in the environment can still cause them to fail.

Some examples of these changes include:

• lighting differences
• object placement changes
• sensor drift
• human behavior
• timing variations

Is the main reason for this that current systems still don't really understand space and the world around them?

Do we really need a lot of different kinds of data, for AI systems that interact with the world?

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Hello, I'm a second year college student, and I'm exploring to find my tech stack or domain.

I want to explore AI/ML path.

Currently my vacations are going on and I'm learning DSA in Java. DSA is essential to be better in problem solving. SQL is also necessary to work with databases, and other tools like Git, GitHub, etc.

Firstly, my focus is on learning (DSA & SQL), then I'll build basic projects and I'll learn to deploy them on GitHub. So, I'll learn Git & GitHub by deploying my projects.

Currently, I'm learning Math required for ML.

Question 1: After watching the lectures, from where should I practice? Please suggest only beginners friendly resources.

I'm learning DSA in Java, after some time, I'll be aware of the logic. So, learning python will be easy. Because I have to learn only syntax as I already know the logic.

Gradually, I'll practice: Python Libraries after a month.

Guide to how to learn and be better in ML.

I’m currently taking a couple of NPTEL courses (for those outside India, NPTEL is a government-backed online platform where IIT professors teach full university-level courses, often pretty mathematically rigorous). I have just completed my 1st year in 2 degees ( CS and DS) and now have a 3 month summer break that I don't wanna waste and build some Projects too along with Mathematical theory.

Right now I’m doing: - second course in Linear Algebra and a Regression Analysis / Linear Models course

And I had this idea that I wanted some opinions on.

Instead of just “finishing” the courses, I was thinking of learning week-by-week and trying to implement small systems based on whatever I’ve learned so far.

For example:

As I go through linear algebra topics like: - vector spaces, linear maps ,projections ,eigenvalues ,SVD

…I gradually try building a very small educational linear algebra engine / mini-NumPy from scratch.

Not because I think I can build something remotely close to actual NumPy, but because I feel like struggling through:
- matrix operations, decoposition methods, numerical issues, performance bottlenecks, stability problems might teach me a lot more deeply than only using high-level APIs.

Similarly, with the regression course, I was thinking of eventually building a small regression library from scratch (OLS, diagnostics, regularization, etc.) kind of inspired by sklearn’s regression modules.

And I want to document the process as blogs/dev logs:

• what broke
• what confused me
• numerical issues I ran into
• why certain algorithms are implemented the way they are
• what I learned about the math/computation behind these libraries

My question is:

Do you think this is actually a valuable way to learn ML/math/programming systems? Or is this one of those things that sounds cool in theory but ends up being a massive time sink with low practical return?

I’m mainly interested in: building deeper intuition and understanding what’s happening under the hood and becoming better at mathematical/computational thinking and hopefully becoming stronger for ML internships/research later on

Would love honest opinions from people who’ve tried similar things.... and also also, will it look good on the Portfolio.... I have a feeling it will be a good differentiator in portfolo and something I can grow in futue when I am done with Low Latency Systems...

Syllabus Links
Second Course in Linear Algebra
Regression Analysis

I’ve been looking for a way to estimate the distribution of the training data, or alternatively, to estimate the uncertainty of network training of a particular class. That way, we can select data that is more beneficial for model training. Does anyone have any suggestions or experience with this?

Hey everyone, I’m a student and I want to become really good in AI/ML over time, not just learn basics. I know some Python but I’m confused about what to learn next and in what order.

Can anyone share the roadmap they followed or what they’d recommend if starting now? Like math, ML, deep learning, LLMs, projects, etc.

Also what skills actually matter to build real AI apps/products instead of only doing courses?

Has anyone received the India AI fellowship email recently or in the past? Please let me know.

Hello everyone, I’m a 2nd year computer science student, and recently I’ve been feeling extremely anxious about my future, since everyone keeps talking about how competitive the field is and how hard you need to “grind” to get a job. I was thinking about AI(ML more specifically). But I’m not sure what I should do. I want to be prepared,but I also don’t want to waste my time in front of the computer for too long🥲 What can I do know, so I’ll be confident in future. Also is ML really that competitive? Or is just big companies?

Trying to figure this out and could use some pointers.

I'm feeding sales call transcripts into Gemini and asking it to pull out patterns that correlate with whether the rep booked a meeting. What I get back is stuff like "asks follow-up questions" or "uses social proof". Technically correct but useless because every rep does these to some degree.

What I actually want is patterns like "asks about urgency right after a price objection" or "names a competitor only after the lead mentions budget". Specific moves in specific spots. The LLM seems to default to category labels even when I ask for verbatim quotes and context.

Two things I think are going on:

The model groups things during extraction. Even when I tell it to keep the exact phrasing it still slaps a generic label on top, and when I aggregate across calls the specifics get lost behind the label.

I don't think my prompting is forcing the specificity hard enough. Saying "be specific" doesn't really work. I've tried giving examples of good vs bad outputs and it helps a little but not enough.

Things I'm thinking about trying:

Skip the LLM label entirely. Just keep the verbatim quote plus some context (what phase of the call, what came right before). Then embed all the quotes and cluster them, and let the clusters be the patterns instead of the LLM-assigned labels.

Two-pass extraction. First pass pulls candidate quotes. Second pass takes a batch of similar quotes and writes a tight description of what they have in common.

Use a stronger model just for the labeling step and see if the specificity changes.

Has anyone done something like this? Particularly interested if you've found a prompt pattern that reliably gets phrase-level output and not category-level. Also curious if there's a name for this problem in the literature, feels like it should have been studied but I haven't found the right keywords.

Hey everyone,

I’m studying something close to bioinformatics/computational biology, so my work is mostly:

•Python/R/SQL coding

•data analytics

•some ML

•datasets + research papers + too many tabs open 😭

•I’m stuck between a Windows laptop and a MacBook with a budget around $2500.

•Windows laptops now have:

•pretty good battery life

•USB-C/power bank charging

•better ports/upgradability

•stronger specs for the price

•But MacBooks still seem unbeatable for:

•battery life

•UNIX/macOS workflow

•stability

•thermals/noise

•overall research/dev experience

I’m not a hardcore gamer — I just want the machine that’ll make coding and research life easier for the next 4–5 years.

People working in bioinformatics/data science/ML:what would you actually choose today and why?

MacBook Pro/Air?ThinkPad/XPS/Zephyrus/etc.?

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So, I’ve learned CNNs theoretically, but now I want to see how they behave practically , specifically on images: where they work well, where they fail, and how to improve their performance, etc.

So, please suggest some resources or projects through which I can explore this practically.

Hi all, I'm at the beginning of ML journey and have a task to find some performance of stocking locations based ONLY on attributes like inbound outbound qty, square feet capacity, load rate, etc...

I know that making a regression model doesn't make sense without label data, but I need to find some sort of performance 0-100 if I have attributes and weight for every attribute.

Please help me understand what the best approach is since I can not evaluate the score.

Can some unsupervised methods help me to group stocking location in two classes >= 0.5 and < 0.5 ?

I am student that still in school but want to start learning AI even before college, i learned python and i have several questions i appreciate if you answer what you know and what you have experienced

1: first of all to get relaxed, Can really one work in a company without certificate?

2: How much time it took you to start doing ML projects and understanding them

3: How much time did it took you to start doing high level projects

4:How many hours have you been practicing daily

5: What are the most challenges you have faced

6: Just tell me that you really benefit from and i prefer to be free and to mention what it gave you knowledge

7: Did you got confused at first with math being used with programming and How did you overcome this problem becuase this is what preventing me to continue although i didnt try yet that hard with math, And shall just to know the basics at first and then be better, I wish here to share the experience that you made,like what you did after each idea in math you knew it, what projects have you done to assure that you have understood it well

8: The last one, is just for curiosity, How much is your sallery,jist range if you dont want to give real sallery, i dont want to learn jjst for money,but of course it will still one of the purpose beside having fun in this field

I've been learning computer vision and deep learning for a while now — nothing extraordinary, just my personal experience. Here are some practical tips I wish I knew when I started reading papers:

1. Get comfortable with set theory notation first

Before diving into papers, spend an hour on basic math notation — ∈, ∀, ∃, ⊆, ∪, ∩, and the common function mapping arrows (f: X → Y). Papers assume you're fluent in this language, and pausing to decode every symbol kills momentum.

2. Don't get stuck on equations — read through first

You'll hit formulas that look like alien scripture. Trust the authors. They've already verified their proofs (often in the appendix) and run experiments to back their claims. Read the sentence as-is, accept it provisionally, and finish the whole paper before circling back. Understanding deepens with context, not with staring harder.

3. Always identify input and output shapes

This is the single most useful habit I've developed. Before worrying about the fancy architecture in the middle, write down: what is the input tensor shape? What is the output tensor shape? For example, an MNIST classifier → input is (N, 28, 28, 1), output is (N, 10). Everything in between is just a transformation pipeline connecting these two. This alone demystifies 80% of papers.

4. Read the code — every line (if available)

Open-source code is the real paper. The paper tells you the story; the code tells you what actually happened. When you want to combine ideas from multiple papers into your own model, you need to know how to implement them. The ability to translate equations into code is the skill that compounds over time.

5. Start with the classics — even if they're "old"

R-CNN, U-Net, ResNet, YOLO — they're easier to understand, have tons of explanations written by others, and give you a confidence boost when you actually get them. Modern papers are often combinations of building blocks from these classic works, so you'll end up chasing their references anyway. Build the foundation first.

6. Avoid mathematically dense papers too early

WGAN, SNGAN, neural ODEs — these go deep into theory and can crush your self-efficacy if you hit them too soon. (If you're strong in math, ignore this. But for the rest of us... save them for later.)

7. Learning is stair-shaped, not linear

You'll plateau for weeks, then suddenly jump. Then plateau again. This is normal. Don't quit during the plateau.

Hope this helps someone starting out. What tips would you add from your own experience?

I want to learn Numpy, Pandas, Matplotlib in order to be ready to understand Machine Learning.

But I wonder which platform to use. Should I use YouTube, Coursera, Udemy or others?

For context, I wanna study robotics and automation so I need to understand a bit of AI to do so.

Thank you so much.

i thinks this problem every new student want to learn AI is facing especially at first, when i ask any chatbot about a roadmap to learn AI he gives that i should learn math and i dont have any problem with that, but iam not understanding how to combine math with programming,is this just at first,and if someone have passed this problem please help me and give me the steps that you have made to make it over, i want to oppen a channelcon youtube to document my journey in AI so any help is appreciated

So I've been stuck in my head as ai is taking jobs already and after agentic ai we all will be fucked. So I thought making my own startup but I don't have any idea So drop some ideas for me and also my friend has started his own startup and his company got registered too. He is working on providing security to other companies from dpdp law which will be initiated in India from this year or next year. Most people never heard of that law and he is find that problem and is working to solve that. Like this please help me to get any idea.