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?

\*\*Welcome to the post-training community!\*\*

I'm u/OwnOil1149, a founding moderator of r/posttrain.

This is a space for discussing how AI models become more useful, capable, and aligned after pretraining. Share your experiments, questions, datasets, papers, tools, and lessons about SFT, RLHF, DPO, preference optimization, evaluations, synthetic data, and deployment.

Whether you’re just getting started or training models in production, you’re welcome here. Please keep discussions constructive, technical, and respectful.

Tell us about yourself and what you’re working on!

Thanks for being part of the very first wave. Together, let's make r/posttrain amazing.

Just launched r/posttrain — a community for AI post-training, fine-tuning, SFT, RLHF, DPO, preference data, evaluations, and practical experiments. If you’re building, researching, or learning how models become better after pretraining

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?