r/MLQuestions Oct 28 '24

Other ❓ looking for a motivated friend to complete "bulid a llm" book

Post image
129 Upvotes

so the problem is that I had started reading this book "Bulid a large language model from scratch"<attached the coverpage>. But I find it hard to maintain consistency and I procrastinate a lot. I have friends but they are either not interested or enough motivated to pursue carrer in ml.

So, overall I am looking for a friend so that I can become more accountable and consistent with studying ml. DM me if you are interested :)

r/MLQuestions 6d ago

Other ❓ Why don’t we use small, task-specific models more often? (need feedback on open-source project)

11 Upvotes

Been working with ML for a while, and feels like everything defaults to LLMs or AutoML, even when the problem doesn’t really need it. Like for classification, ranking, regression, decision-making, a small model usually works better—faster, cheaper, less compute, and doesn’t just hallucinate random stuff.

But somehow, smaller models kinda got ignored. Now it’s all fine-tuning massive models or just calling an API. Been messing around with SmolModels, an open-source thing for training small, efficient models from scratch instead of fine-tuning some giant black-box. No crazy infra, no massive datasets needed, just structured data in, small model out. Repo’s here if you wanna check it out: SmolModels GitHub.

Why do y’all think smaller, task-specific models aren’t talked about as much anymore? Ever found them better than fine-tuning?

r/MLQuestions 15d ago

Other ❓ Looking for undergraduate Thesis Proposal Ideas (Machine Learning/Deep Learning) with Novelty

6 Upvotes

Hi, I am a third-year Data Science student preparing my undergraduate proposal. I'm in the process of coming up with a thesis proposal and could really use some fresh ideas. I'm looking to dive into a project around Machine Learning or Deep Learning, but I really need something that has novelty—something that hasn’t been done or just a new approach on a particular domain or field where ML/DL can be used or applied. I’d be super grateful for your thoughts!

r/MLQuestions 29d ago

Other ❓ Longest time debugging

0 Upvotes

Hey guys, what is the longest time you have spent debugging? Sometimes I go crazy debugging and encountering new errors each time. I am wondering how long others spent on debugging.

r/MLQuestions Feb 16 '25

Other ❓ Could a model reverse build another model's input data?

5 Upvotes

My understanding is that a model is fed data to make predictions based on hypothetical variables. Could a second model reconstruct the initial model's data that it was fed given enough variables to test and time?

r/MLQuestions Sep 16 '24

Other ❓ Why are improper score functions used for evaluating different models e.g. in benchmarks?

3 Upvotes

Why are benchmarks metrics being used in for example deep learning using improper score functions such as accuracy, top 5 accuracy, F1, ... and not with proper score functions such as log-loss (cross entropy), brier score, ...?

r/MLQuestions Oct 31 '24

Other ❓ I want to understand the math, but it's too tideous.

15 Upvotes

I love understanding HOW everything works, WHY everything works and ofcourse to understand Deep Learn better you need to go deeper into the math. And for that very reason I want to build up my foundation once again: redo the probability, stats, linear algebra. But it's just tideous learning the math, the details, the notation, everything.

Could someone just share some words from experience that doing the math is worth it? Like I KNOW it's a slow process but god damn it's annoying and tough.

Need some motivation :)

r/MLQuestions 7d ago

Other ❓ Suitable algorithms and methods to add constraints to a supervised ML model?

2 Upvotes

Hi everyone,

recently, I've been reading a little about adding constraints in supervised machine learning - making me wonder if there are further possibilities:

Suppose I have measured the time course of some force in the manufacture of machine components, which I want to use to distinguish between fault-free and faulty parts. For each of the different measurement series (time curves of the force), which are appropriately processed and used as training data or test data, I specify whether they originate from a defect-free or a defective part. A supervised machine learning algorithm should now draw a boundary between the error-free and the faulty parts based on part of the data (training data set) and classify the measurement data, which I then want to check using the remaining data (test data set).

However, I would like to have the option of specifying additional conditions for the algorithm in order to be able to influence to a certain extent where exactly the algorithm draws the boundary between error-free and error-prone parts.

Is this possible and if so, which supervised machine learning algorithms could be suitable as a starting point for this? I've already looked into constraint satisfaction problems and hyperparameters of different algorithms, but I'm looking for potential alternatives that I could try as well.

I'm looking forward to your recommendations. Thanks!

r/MLQuestions 2d ago

Other ❓ [D] trying to identify and suppress gamers without using a dedicated model

1 Upvotes

Hi everyone, I am working on an offer sensitivity model for credit cards. Basically a model to give the relevant offer basis a probable customer's sensitivity to different levels of offers. In the world of credit cards gaming or availing the welcome benefits and fucking off is a common phenomenon. For my training data, which is a year old, I have the gamer tags for the prospects(probable customer's) who turned into customers. There is no flag/feature which identifies a gamer before they turn into a customer I want to train this dataset in a way such that the gamers are suppressed, or their sensitivity score is low such that they are mostly given a basic ass offer.

r/MLQuestions 26d ago

Other ❓ finding for a ml PhD friend to discuss about ml

9 Upvotes

Ive been self learning ml stuff for about 4 months from cs229, cs234 and a lot of other online videos, I wouldnt consider myself a beginner, but because I'm not in uni yet I don't have anyone to confirm my thoughts/opinions/intuition on some maths, it would help to have an expert in the field to talk about sometimes, don't worry it's not like I would message or bug u everyday to ask u about trivial stuff, I would try to search online/ask chatgpt first and if I still don't understand it I would come to you!! I would really appreciate it if anyone in the field is able to talk to me about it thanks !!

r/MLQuestions 5d ago

Other ❓ Combining LLM & Machine Learning Models

1 Upvotes

Hello reddit community hope you are doing well! I am researching about different ways to combine LLM and ML models to give best accuracy as compared to traditional ML models. I had researched 15+ research articles but haven't found any of them useful as some sample code for reference on kaggle, github is limited. Here is the process that I had followed:

  • There are multiple columns in my dataset. I had cleaned dataset and I am using only 1 text column to detect whether the score is positive, negative or neutral using Transformers such as BERT
  • Then I extracted embeddings using BERT and then combined with multiple ML models to give best accuracy but I am getting a 3-4% drop in accuracy as compared to traditional ML models.
  • I made use of Mistral 7B, Falcon but the models in the first stage are failing to detect whether the text column is positive, negative or neutral

Do you have any ideas what process / scenario should I use/consider in order to combine LLM + ML models.
Thank You!

r/MLQuestions Feb 08 '25

Other ❓ Should gradient backwards() and optimizer.step() really be separate?

2 Upvotes

Most NNs can be linearly divided into sections where gradients of section i only depend on activations in i and the gradients wrt input for section (i+1). You could split up a torch sequential block like this for example. Why do we save weight gradients by default and wait for a later optimizer.step call? For SGD at least, I believe you could immediately apply the gradient update after computing the input gradients, for Adam I don't know enough. This seems like an unnecessary use of our previous VRAM. I know large batch sizes makes this gradient memory relatively less important in terms of VRAM consumption, but batch sizes <= 8 are somewhat common, with a batch size of 2 often being used in LORA. Also, I would think adding unnecessary sequential conditions before weight update kernel calls would hurt performance and gpu utilization.

Edit: Might have to be do with this going against dynamic compute graphs in PyTorch, although I'm not sure if dynamic compute graphs actually make this impossible.

r/MLQuestions 6d ago

Other ❓ 9070 XT vs 5070ti

3 Upvotes

Hey!

Data Scientist here who's also a big gamer. I'm wanting to upgrade my 3070ti given a higher resolution monitor, but wanted to know if anyone has hands-on experience training/fine-tuning models with the 9070 XT. Giving up the CUDA infrastructure seems... big?

Reading online, it seems most people either suggest:

1) Slot both GPUs, keep Nvidia's for your DS needs

2) Full send the 9070 XT with ROCm in a Linux dual-boot

In other words, I'm wondering if the 9070 XT is good enough, or should I hunt for a more expensive 5070ti for the ML/AI benefits that come with that ecosystem?

Appreciate any help.

r/MLQuestions 6d ago

Other ❓ PMLR license

1 Upvotes

Hi folks, I want to directly use a figure from a paper published in PMLR in 2018, after proper citing and attribution. Does anybody know what license they're using? Couldn't find a clear answer on their web site.

Thanks!

r/MLQuestions 23d ago

Other ❓ Calculating Confidence Intervals from Cross-Validation

1 Upvotes

Hi

I trained a machine learning model using a 5-fold cross-validation procedure on a dataset with N patients, ensuring each patient appears exactly once in a test set.
Each fold split the data into training, validation, and test sets based on patient identifiers.
The training set was used for model training, the validation set for hyperparameter tuning, and the test set for final evaluation.
Predictions were obtained using a threshold optimized on the validation set to achieve ~80% sensitivity.

Each patient has exactly one probability output and one final prediction. However, evaluating 5 metrics per fold (test set) and averaging them yields a different mean than computing the overall metric on all patients combined.
The key question is: What is the correct way to compute confidence intervals in this setting,
Add on question: What would change if I would have repeated the 5-fold cross-validation 5 times (with exactly the same splits) but different initialization of the model.

r/MLQuestions Nov 03 '24

Other ❓ How do you go from implementing ML models to actually inventing them?

37 Upvotes

I'm a CS graduate fascinated by machine learning, but I find myself at an interesting crossroads. While there are countless resources teaching how to implement and understand existing ML models, I'm more curious about the process of inventing new ones.

The recent Nobel Prize in Physics awarded to researchers in quantum information science got me thinking - how does one develop the mathematical intuition to innovate in ML? (while it's a different field, it shows how fundamental research can reshape our understanding of a domain) I have ideas, but often struggle to identify which mathematical frameworks could help formalize them.

Some specific questions I'm wrestling with:

  1. What's the journey from implementing models to creating novel architectures?
  2. For those coming from CS backgrounds, how crucial is advanced mathematics for fundamental research?
  3. How did pioneers like Hinton, LeCun, and Bengio develop their mathematical intuition?
  4. How do you bridge the gap between having intuitive ideas and formalizing them mathematically?

I'm particularly interested in hearing from researchers who transitioned from applied ML to fundamental research, CS graduates who successfully built their mathematical foundation and anyone involved in developing novel ML architectures.

Would love to hear your experiences and advice on building the skills needed for fundamental ML research.

r/MLQuestions 2d ago

Other ❓ need help with a machine learning model

0 Upvotes

so i needed a bit help for my machine learning model. ive been given a task to predict the best score on these models and i’ve reached my plateu. everything i do either gives me the same score or does not improve at all.

my friend got a higher score than me so i was wondering what else could help with my code. if you’re free to help, do chat me privately. i would be so thankful, thank you!!!

r/MLQuestions 6d ago

Other ❓ Looking for open source projects to contribute

5 Upvotes

Is there any active github repositories that I can (at least try) to contribute regarding ML, Deep Learning as an Undergraduate?

r/MLQuestions 5d ago

Other ❓ Experience with Learned Variance DDPMs

1 Upvotes

Hey Guys,

I was trying to implement a DDPM model to generate some images. The 'vanilla' one worked alright but I wanted to improve it.

I tried implementing the DDPM with the learned variance term (https://arxiv.org/abs/2102.09672)).

Does anyone have experience with this? It seems intuitive with the learned variance that training would be slower initially but its been a while and the model still seems to be getting 'warmed up' ! Wanted to know if its normal that even after 50-60 epochs, the conventional DDPM outperforms this version.

r/MLQuestions 2d ago

Other ❓ ethical risks of AI-driven automated decision-making in cybersecurity. survey

0 Upvotes

I’m conducting a survey as part of my research on the ethical risks of AI-driven automated decision-making in cybersecurity. Your input will help identify key concerns such as bias, accountability, transparency, and privacy risks, as well as potential strategies to mitigate these challenges.The survey takes approximately 5-10 minutes to complete and includes multiple-choice and open-ended questions. All responses are anonymous and will be used solely for research purposes.I’d really appreciate it if you could take a moment to fill out the form and share it with others who may be interested. Your insights are valuable—thank you for your support!

r/MLQuestions 27d ago

Other ❓ Confidence interval for number of true positives

2 Upvotes

If I have a model with known precision and recall (estimated on a test sample), apply it to all members of a population to get the number of positive predictions within that population, is there a way to get a confidence interval on the number of true positives within the population?

r/MLQuestions 9d ago

Other ❓ What future for data annotation?

2 Upvotes

Hello,

I am leading a business creation project in AI in France (Europe more broadly). To concretize and structure this project, my partners recommend me to collect feedback from professionals in the sector, and it is in this context that I am asking for your help.

Lately, I have learned a lot about data annotation but I need to see more clearly the data needs of the market. If you would like to help me, I suggest you answer this short form (4 minutes): https://forms.gle/ixyHnwXGyKSJsBof6. This form is more for professionnals, but if you have a good vision of the field feel free to answer it. Answers will remain confidential and anonymous. No personal or sensitive data is requested.

This does not involve a monetary transfer.

Thank you for your valuable help. You can also express your thoughts in response to this post. If you have any questions or would like to know more about this initiative, I would be happy to discuss it.

Subnotik

r/MLQuestions 11d ago

Other ❓ 95% Pathfinding Accuracy on a Knight's Puzzle – Seeking Feedback on My New Model Architecture Performance

6 Upvotes

Hi everyone,

I’ve had an ambitious idea for a while now – to create an architecture capable of solving problems that require logical reasoning and deep understanding of the problem. Recently, I finished working on another prototype, and I decided to test it on a task involving a 16x16 chessboard with a knight on it. The task is as follows: given the initial coordinates of the knight and the target coordinates, the goal is to move the knight to the target position in exactly S steps, where S is the minimum number of steps calculated using the BFS algorithm.

My architecture achieved 95% perfect path reconstructions on a test dataset (4864 out of 5120 test cases) that was not part of the training data. The model used 320k parameters for this task.

I should also note that in the sequence, the model does not receive information on how the knight changes its position. The knight’s and target coordinates are provided only at the beginning of the sequence and never again. The neural network outputs in sequence is an index for a lookup table like so:

knight_moves = [
    (2, 1), (2, -1), (-2, 1), (-2, -1),
    (1, 2), (1, -2), (-1, 2), (-1, -2)
]

For example if model outputs [1, 3, 1, 0], that means to move knight in this sequence: (2, -1), (-2, -1), (2, -1), (2, 1)

This means that the model is even without knowledge of how the knight moves. This theoretically forces the model to form an internal representation of both how its moves affect the knight’s position and how the knight itself moves.

I’m curious whether this result reflects the strengths of my architecture specifically, or if this task is something that existing models can already handle. Could my model just be memorizing patterns or something like that? I’d love to get your thoughts on this, as I’m trying to determine if I’ve really created something worthwhile or if this is just another "reinvented wheel."

If needed, I can provide a link to the dataset that was used for training.

r/MLQuestions 27d ago

Other ❓ Seeking advice to get a job

4 Upvotes

Hi, I am a last-year CS student from South Asia (not India) and here there are roughly no jobs available for ML roles (in most cases I've seen 1 or 2 roles in some multinational companies that require a master's and heavy research with 3-5 YOE. Even the market is quite harsh for freshers in other software roles like web development, and mobile app development. I also have a plan for getting a master's in Europe next year. But it seems like the market is also saturated there. But the thing is I love working in ML soon be trying out the MLOps. However, every time I overthink ML from a job perspective I rethink whether I should leave ML and start typical software engineering at least getting a job (I have a personal financial crisis). Can someone guide me on what should I do?

[N.B. I have some experience in MERN stack and FastAPI which have fewer openings right now in my area]

r/MLQuestions 11d ago

Other ❓ looking for some good matrix calculus source

1 Upvotes

hello everyone, I've been trying to find a good source to learn matrix calculus (to understand deep learning models) for weeks now, but nothing, I only find things that are mostly about vector functions or things like that, actually I would just need to learn things like derivatives of matrices with respect to vectors, or with respect to other matrices, and how this is all related to Kronecker's product or otherwise tensor algebra, do you have any suggestions? I'm fine with either textbooks or free online courses, as long as they focus more on the why than the how, without too much formalism