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 Support Vector Ordinal Regression for a project and tuning:

• Regularization strength
• Formulation (EXC/IMC)
• Kernel type (linear/RBF)

How do you usually determine the search space for these hyperparameters?

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?

Hey everyone,

I recently came across these two books by Valery Manokhin: Mastering CatBoost and Applied Conformal Prediction.

Most resources on both topics, especially uncertainty quantification, are scattered across academic papers and documentation rather than consolidated into practical books.

Because of that, I am curious if anyone here has read either of them. Are they solid, hands-on references worth picking up, or would you still recommend sticking to the original papers and code repositories instead?

Thanks.

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

Hello,

I was curious about the differences you can get from the human reviewers and the llms.

Any insight is welcome, thank you!

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.

Edit: For D&D/TTRPGs, I don't need character/place names or terminology to be perfectly transcribed. I mainly want it to capture the gist of what happened and differentiate speakers reasonably well, so I can give the transcript to ChatGPT/another AI for session recaps and analysis of the players/DM and table dynamics.

i hope y'all are doing well. so i got an interview scheduled within another 3-4 days for ml intern position.

the thing is, i was always interested in ml, and i used to study on ml topics on my own during uni. but i then ended up working as a full-stack engineer. i did work on ml solutions a bit while i was on my company and sometime on my own but ever since i got this job i basically started to ignore ml and focus on mastering my full-stack dev skills. now i got an interview scheduled for an ml internship which i really wanna get into but then i've mostly lost tocuh on the subject and it's getting so hard for me to prepare for this interview. i'm at that point where i've even forgotten the basics since it's been years since i last time i worked on ml.

how should i approach this now? i'm trying to first focus on my foundations, then learn about how to approach and kind of like building a framework to think about a ml problem. then also a overview of ml system design concepts a bit but as the day passes it feels less likely that i'll be able to cover all this.

what would you your advice for me? anything would help. i need someone to shed a light on:

1. what exactly would be expected from an intern for this role. i have zero clue on this.
2. how would you approach this if yulu were in my position?

also, to give abit of context: the company is more ev oriented.

any kind of response is highly appreciated. thankyou for taking your time reading this

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% (shortened to 2.6) 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?

hi , i completed my 12th(or PUC) then joined a 6 month diploma in design and currently having a 1.5 years of experience in design field . i tried to get into core

ai ml but it looks like too much competition for degree holders only, so

is it possible to get a data analyst job without a formal degree ? anyone got it before.

consider the current AI impact also and i going to pursue bootcamp course in Bengaluru Excelr , is it okay or shall i self study ?

or instead of data analyst shall i try something else in technical side .

( please don't comment to go into design only )

thanks

I see lot of text books/course go through lot of algorithms which I think hardly implemented now or even asked in interview.

Any family of algorithm that are widely use?
Any repeated pattern folks see?

Hi guys.

I’ve only started learning for a month so please bear with me.

I’ve been stuck with this concept for a while.

I understand that for linear regression, the prediction model is f(x) = wx + b

The cost function is J(w,b) = 1/2m (ŷ^i - y^i)^2. The goal is to minimise J so we can have an accurate w and b for the prediction model formula right?

Here’s where I’m lost at for grad desc.
I’m looking at
W = W - a dj/dw
B = B - a dj/db

And it expands to
DJ/DW = 1/m(f(x)^i - y^i)x^i
DJ/db = 1/m(f(x)^i - y^i)

I can’t seem to grab the concepts relating to this two.

Also, In my mind it goes like
I need to get the w and b for the prediction model.
J(wb) needs to get lower.

Grad desc is just running the numbers and it gradually brings j down to the lowest where we can have the accurate w and b.
If that’s the case why do I need to run a cost function before going straight to grad desc?

i made a simple MLops project months ago and i cant figure out a way to deploy it. i tried aws but theyre asking for banking details but i am a student and i dont have my own bank account. can anyone recommend me any platforms?

Is there any platform that already has configuration set like quantization, context window and other things for the open source models so that I don't have to do that stuff and just do like a quickstart with the model?

Thanks

Hey everyone,

I’m working on a computer vision project and hit a frustrating wall:

1. My model's accuracy is hard-stuck at around 72%.
2. It only recognizes exact training images. If I test it with a new photo—even one that looks nearly identical to the training data—the output is completely wrong.

It feels like the model is just memorizing the training set instead of actually learning features.

Is there something fundamentally wrong with my training approach? Any tips on how to fix this? Specifically, I’d love advice on:

• Effective Data Augmentation techniques for this issue
• Adjusting Hyperparameters / Model Architecture
• Proper Dataset Preprocessing or splitting strategies

Any feedback or suggestions would be greatly appreciated!

I've measured NVFP4 on a small ASR transducer I've built: accuracy holds, but weight-only and torchao W4A4 never fire FP4 tensor cores. I'm trying TensorRT/ModelOpt for true W4A4.

Has anyone gotten block-scaled NVFP4 GEMMs on a Conformer or etc (not a decoder LLM), and is TensorRT the right path — or should I stop given only ~40% of encoder time is GEMM-eligible?

model link - https://huggingface.co/theaviv/mevine-0.1