I’m working on a student(beginner) focused on vehicle speed estimation using YOLO + tracking (likely ByteTrack/OpenCV). I initially looked into BrnoCompSpeed, but the dataset size is extremely large (~200GB+) and difficult for me to handle on limited storage and internet.I mainly needed datasets on which i can run my codes and also check if they are giving correct answers or not
So i have to make a presentation on transformers, and the majority have no idea of ML or even embeddings,
How do i explain what the attention mechanism is, even if I don't go into deep theory i need to explain the attention mechanism. I'm going to teach them like, it is an algorithm by which an AI reads all words at once and decides the relationship between them.
Share your intuition .
Hii...what Al tools are you using to write academic papers (9000+ words). Skip chat gibbidy and gemini, they are not up to that level (from what I experienced).
I used claude and it's good, but reiterations and redrafting without the 100$ subscription feels like a pain in the ass.
I've heard Antigravity is doing a pretty good job, but it feels a little technical to use.
Help out a brother here and suggest me something real good you have stumbled upon.
It is well known that LLMs can over acknowledge, agree, flatter, and please its subscriber or primary user. This can result in the disservice to the user when they only receive agreements rather than being appropriately challenged. This is particularly notable when LLMs are used for quasi-counseling or analyzing discussions between two people.
As such, please help me write a prompt to instruct any LLM to cut it out! No sycophancy, taking sides, flattering, echo-chamber, "yes-man", assumptions, and improve objectivity, brutal honesty, neutrality, and real-world verity.
Thank you.
i have been taking this course, the best in teaching the theory, but the coding labs are a hassle to me. I had to understand and code linear regg all by myself by taking help from ai and logistic regg coding labs are driving me crazy can someone suggest me how do i get better at the coding part of it
Really need help with solving this paper’s problem to avoid data leakage. I need to think of a way to deal with overlapping nodes while splitting by edges. I’m thinking of creating 2 graphs
(1) training graph where loss and metrics are scored on nodes that have a timestamp =< cut off timestamps. Overlapping nodes will still appear in graph but take no roles other than message passing.
(2) inference graph where metrics are scored on future nodes and overlapping nodes.
(Noted: most “message” are from edges)
Is this okay?
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.
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.
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:
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?
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:
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
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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’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?
I went all in on portfolio projects and then got an interview with a company for an MLE position. They found me on LinkedIn and reached out (my first time that happened, what an experience), but I feel like my technical screen would have been way stronger if I'd done more small-scale coursework-like problems. I feel like there's just a muscle memory that I was missing, since my portfolio projects are more regression-focused and they wanted a classification model.
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?
Has anyone received the India AI fellowship email recently or in the past? Please let me know.
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.?
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 ?
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.
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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.
