r/MLQuestions

I used a transformer model to generate normalized 768-dimensional float32 vectors from 3 million sentences. This rounds up to roughly 9 GB of data. To progress further in my analysis, I want to cluster the data to highlight different key points and trends.

Now I initially wanted to apply PCA to the dataset, as 768 dimensions is pretty much, but the transformer model generated embeddings with not extreme enough covariance.
Thus, PCA could change the semantics too much for an analysis to be accurate.

I went directly to clustering algorithms and initially tried to apply sklearn.cluster.HDBSCAN which would have needed way too much RAM---around 72 TBi if I remember correctly. Then I tried out sklearn.cluster.OPTICS, which "only" needed about 70 GB, but as I "only" have 32 GB of RAM I needed to use 50 GB swap space, which didn't go well as you can imagine.

Does it make sense to try out k-means clustering, should I go to 16-bit floats for the embeddings, or still apply PCA?

Should I try out something else in this context?

Note: As this question was flagged "off topic" on Stack Overflow, I'm gonna try here ;)

When I started focusing on practical mathematical implementation of machine learning I found that I lack so very math basics(I blame my school for that) so I tried making my way through basics to go deep into machine learning and while I was learning from professor Leonard on YouTube someone recommended me this "Mathematics for machine learning" by Marc peter. Tbh I dont understand shit in this book, I genuinely get overwhelmed by this book. I dont understand is it only me ? Am I that dumb in maths?

Well I need to get on track asap really! Suggest me something and please share your opinion

Y'all see that it protects the privacy of the client but does it actually? Attackers can try to hack the model and reverse engineering might be possible to rebuild the data. Not just that, attackers even manipulate the model by either poisoning the data or the model. Some use multiple accounts to shape the model their own way.

Their are many ways the models can be hurt to avoid convergence, so how do they actually protect their models? I would love to discuss it with people who have read about this.

Thank you

Hi guys so recently I got into this stuff playing with various local llm models and trying out mcp's and other interesting stuff like heretic versions and all.

However as I am diving deep, I am not able to understand a lot of terms like top p, k, inference, training, llama cpp, presence, repeat penalty, gguf and hardware thing like layers, universal kv etc etc which I want to tune as per my liking or atleast understand how they effect the responses.

Now I am a complete newcomer to this stuff, not in AI like rag and stuff but more towards learning and understanding this terminology. Is there any guide someone can provide which can help me understand this stuff? I know this may be a part of ML or deep learning but I may not want to go that route and learning in that domain purely but just enough so I can play with models that community provides on huggingface for instance. Just enough knowledge so I can understand what author has changed, and what diff diff terminology or methods or this llama, vllm stuff means and what are all these overwhelmimg settings on lm/unsloth studio.

Thanks in advance.

Hey everyone!

I’m planning to start learning Machine Learning by actually building projects instead of spending too much time going through courses and theory before building anything.

I already know Python, NumPy, Pandas, Matplotlib, and Seaborn, and I also have some experience with data collection and data cleaning. Right now, I’m working on my probability and math fundamentals as well.

My long-term goal is to become an AI/ML Engineer, so I want to learn ML in a practical way and gradually work my way from beginner projects to more advanced ones.

I’d really appreciate some suggestions from people who have already gone through this:

• What ML projects would you recommend starting with?
• How should I progress from beginner → intermediate → advanced?
• Are there any projects that actually helped you understand ML concepts deeply?
• I’d especially love GitHub repositories where I can look at good ML projects, learn from the code, and maybe try implementing them myself.
• Any good real-world datasets or project ideas would also be helpful.

I’m not looking for projects where I just load a dataset and call model.fit() 😅. I want projects that actually make me understand why the model works, how to improve it, and how ML is used in a real problem.

If you learned ML through a build-first approach, I’d love to hear what worked for you and what you would recommend to someone starting out.

Thanks! 🙌

Hello! I am currently trying to build the knowledge needed to make an Artificial Intelligence with Python, and I have discovered that there is some tensors involved in this, I learnt matrices (briefly), but apparently math tensors are not the same as CS tensors (whatever that means?), can someone please help me? And I wonder if I even need to know this (well ig so but not sure). And I am not in any math or cs major, but I am in mid-high school, so as you can guess, I did not learn anything tensors related.

Note: for anyone wondering; no I don't have anybody that I could contact which could explain me PyTorch tensors.

Hello friends, how are you? If I had to give a brief information, I studied IT for 4 years and graduated this year. I am 22 years old. In 2024, I started paying attention to data science, I went to the course. After studying for a while, I left the course halfway due to financial reasons and so far I have applied for many vacancies and internship programs. But there was no turning back, there were those who turned back, I was rejected from the interview. I don't want to be unemployed, so I have the idea of ​​changing the field. Sometimes I feel bad, I don't know what to do, where to start.

I’m a rising college sophomore in an ML lab.

I have presented plenty in hs and college but I have found that digesting and presenting complex results on the fly is difficult. Especially when balancing detail and simplicity. Some plots I understand but don’t know where to start in an explanation when I am talking about them to someone even with good knowledge of my project.

Does anyone have tips? Is it just raw amt of experience that helps? Is this hard for everyone?

I come from a more traditional ML/stats background. So far, I’ve mostly used CPU HPC clusters and only used a GPU once.

I’d like to move more into deep learning, especially AI for biology, and I’m curious how much GPU compute people typically use in research.

I know it depends a lot on the field and the project, but for something like a NeurIPS or ICML paper—not training a foundation model from scratch, but working on a smaller multimodal model, GNN, VAE, etc.—how many GPUs do you usually need?

I’m using Random Forest for a project and tuning:

• Number of trees
• Maximum tree depth
• Number of features considered at each split
• Minimum samples required to split a node

How do you normally determine the search space for these parameters?

We train language models to predict next token in pretraining and later fine tune them, but what about exploration and creativity. You can’t really do anything about that because we train the models on specific datasets, right?

I want language models to focus on different solutions for the same problem where the gap between solutions is extremely different.

For example, if I am working on a novel problem and I use existing mathematical functions, would it be possible for language models to neglect them because they might be completely wrong even though they were trained on them? Meaning instead of next token prediction, we would give the model different allowed next tokens or group of tokens and incite them to explore different next tokens for the same training example?

Is there a theory behind what I am asking for?

Hey everyone,

I'm trying to get a better sense of where classic ML fits in the current landscape, dominated by LLMs and deep learning. Are supervised and unsupervised learning still considered important skills/topics to learn in 2026, or have they become mostly a "foundations" step before moving to more advanced techniques?

Also, if you have any book recommendations for Python that cover these topics well, I'd really appreciate it.

Thanks in advance!

i wanna build a text diffusion model which interpret text and convert it into ascii images

so like

Text : build a cat

Output :

/\\\_/\\

( o.o )

\> \^ <

So , i have a decent background of ml algo ( completed cs229 , cs230 , Ml architecture and basic CNN and diffusion model )

ik making a project like this is tricky and making diffusion model like that from scratch is hard but i wanna try it because that's wot make me excited lol ...

I am currently reading GANs research paper , can u guys help me in finding more papers which helps me in making this project or guide me through this good title for this

Thx in adv

Hi guys I started Machine learning from zero knowledge of coding can I do it or not.

Does anyone have good resources and roadmaps pls share it with me..

Basically I try to learn ML to get a role in the AI Related not exactly a ML engineer. So for that learning from the basics like Math concepts and back propagation etc., every topic that used to train our model from scratch is a better method of learning or

2: RAG, LLM related topics , MCP, Agentic AI learn how they actually work instead of going deep into the actual structure(basically exclude the math and how model trained).

Which way of learning is good for future?

Why do I ask this means every job application I go through I only see the latest topic not the core of ML. In my opinion, Learning the upper layer of AI is pretty simple when compared to going deep into math like back propagation,math concepts,and gradient descent etc,. Is spending time on learning everything is worth the time?

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Hi everyone, I am an undergraduate student in my last year. I am not a student of Computer Science or a subject related to it. For my thesis, I want to learn about MACHINE LEARNING. I know the C language up to creating files and writing and reading in these files. According to COPILOT, I need to learn the following-

• Python Basics
• NumPy
• Pandas
• Matplotlib
• Scikit-Learn
• Random Forest
• XGBoost
• MLP (ANN)
• R², MAE, RMSE
• SHAP
• Basic Optimization (GA/Scipy)

Can you share some free resources to achieve my goal?

I'm working on a project related to prediction of power outage during extreme weather events (heatwave, drought, cold storm, etc). I've 5 yrs of data (hourly) in which 18% is positive class (where power outages are 1). I've previously worked on imbalanced datasets and such and achieved high precision and recall simultaneously but this time, it's just not happening.

I've tried, tree based, gradient, ensemble (boosting, hybrid, stacking), even TFT, tabnet, LSTM, Pytorch etc models when my initial framework didn't work. I've tried stratified and k-fold when 80-20 didn't work. I optimized the threshold, even manually checked the metrices at decision threshold from 0.4 to 1.

It's a non-linear problem so I tinkered with feature eng when nothing worked. Spearman correlation doesn't rly work but I checked anyways, the top feature got 9.8% correlation with the target column, and mutual info (MI) score got 13% as top metric.

But, after all these, the best metrices i got was from meta_LightGBM (5-Fold Stacking) is 0.9145 ROC-AUC, 0.4372 PR-AUC, 52.09% precision, and 37.94% recall, and it was the best result I got after threshold tuning.

Can someone explain me what I'm doing wrong, or is there's another method/point I've to take into consideration?

Types of audio:

I often audio-record on my iphone:

• - Important conversations
• - Medical appointments (in-person and online)
• - Dungeons and Dragons and other in-person TTRPG games

I also transcribe a bunch of audio messages/voice notes.

(All recorded ethically and legally)

Workflow:

• I've been using TurboScribe to transcribe everything → I export a PDF that I can search and/or upload to ChatGPT for analysis.
• Probably transcribe like 15-25 hours a month.

Ideally, I'm looking for something that's quick/easy to use and lets me easily download/export transcripts.

I'm interested in a software with similar or better price, especially if it was more accurate with terms (medical, gaming etc.) and recognized multiple speakers better?

Is there anything you'd recommend over TurboScribe for this use case?

I'd appreciate hearing how it compares - what it does better/worse.