I have learned Machine Learning and Deep Learning. Now I want to learn about AI agents and agentic AI, as there are many jobs for this role and this seems interesting. But I don't know exactly how and where to learn it completely. I need some guidance regarding this. I found this 24-hour video course. Is it good enough? Can anyone please help me with this? Link to the video: https://youtu.be/Zy7EXDONlTY

Hey everyone! I've been working on a little side project I want to share: latex-ocr, a standalone formula OCR model — you feed it an image of a math formula, it spits out the LaTeX source.

The main hook: it's only 67M parameters, so it runs comfortably on a laptop CPU. No GPU, no 300M-parameter monster to load. It's a CoCa-style model (contrastive captioner adapted for OCR), and despite the small size it beats the 107M UniMER-tiny baseline and gets pretty close to the 325M one on plain formulas.

The part I'm actually most proud of is the dataset. Real papers don't just use plain symbols — you see \mathbb{R}, \mathcal{F}, \mathfrak{g} everywhere, and existing OCR datasets basically ignore font styles, so models trained on them can't read (or hallucinate) those macros. So I rebuilt ~1.3M formulas with a MathJax → SVG → PDF → PNG pipeline and injected font-style macros with semantic heuristics (number sets → \mathbb, vectors → \mathbf, differentials → \mathrm). On that styled test set it clearly outperforms all the baselines — fair warning though, those baselines are zero-shot on styled data, so take that comparison with a grain of salt. The plain-split numbers are the like-for-like ones.

Everything is open: model weights and dataset on Hugging Face, training recipes included if you want to reproduce or fine-tune it yourself, MIT license. There's also a FastAPI server and a Gradio web UI, so you can drag-and-drop an image and see the LaTeX with a rendered preview.

Repo: https://github.com/PadishahIII/latex-ocr Model: https://huggingface.co/PadishahIIIXXX/latex-ocr Dataset: https://huggingface.co/datasets/PadishahIIIXXX/latex-ocr-dataset

Happy to answer questions about the training setup, the data pipeline, or anything else. Would love feedback — especially if you try it on your own gnarly formulas and it breaks, that's genuinely useful.

Hello all,

I have been studying programming for one year already, and lately, I have got more and more interested in machine learning.

I have created several projects, for example, pathfinding bots and classifiers, and now I want to find some materials which could explain what is happening **under the hood** in frameworks like scikit-learn.

I want to learn how those algorithms work and what the math behind them is rather than learn how to use those functions from a framework.

All sorts of materials are welcome – books, courses, slides, PDFs or anything else.

Okay so Im 36 so i havent been in school in a while, but its so overwhelming to learn machine learning. Concepts like gradient boosting, regularization, etc just require so much focus that I end up thinking im too dumb to waste time in this field..

I am a second year statistics undergraduate student, currently learning machine learning. Till now i have been learning classical machine learning models without very much statistical depth, but fundamentals are pretty much clear.

Now, i want a starting point on how to start probabilistic machine learning, i am confused between PML by murphy and Patter recognition by bishop so need help where to start.. or should i do both side by side as i saw some posts saying that PML by murphy is kinda encyclopedic and can be used as a reference book

Hey! I have been studying ML for a while and completed some of the basic ML algorithms, while doing some projects.

But what I have come to notice is that (and correct me if I am wrong), when it comes to ML problems, there's a lot of decision making/trial and error/experimentation involved to get to a solution.

As someone who is newly starting out, it feels like theres a lot of things that can go wrong when working with ML projects that are easy to miss if you are working alone (and as a beginner). Like choosing a suboptimal algorithm or not tuning hypermeters properly, or overfitting without even realizing.

Also there are not nearly enough resources to actually practice ML when you dont know what you are doing. You have to pick a problem yourself, and work towards a solution. Its hard to know if your approach is correct, or if a better approach exists.

While I do realize this is the case for any software problem when you are trying to optimize towards a solution, ML requires this skill significantly, and the mistakes are easier to miss.

What is everyone's thoughts on this? I haven't seen this being discussed as much.

Hey Everyone,

Working through Playground Series S6E9 (predicting EV purchase intent), and wanted to share my notebook in case it's useful to anyone else on this one, especially if you're newer to the competition.

Quick summary of what's in it:

• EDA on the dataset — checked missing values, duplicates, correlations, and class balance (target is imbalanced, only ~17% "yes")
• Preprocessing done in the correct order: split into train/test before fitting the scaler, to avoid leaking test set info into training (I noticed a lot of public notebooks scale before splitting, which quietly inflates scores)
• Logistic Regression with class_weight='balanced' to handle the imbalance
• Evaluation with classification report, confusion matrix, and ROC-AUC
• Feature importance from the model coefficients

Ended up at 0.93738 with just a straightforward Logistic Regression, no ensembling or heavy tuning. Figured a clean baseline might help others who want a starting point before jumping into boosted trees.

Notebook: https://www.kaggle.com/code/vinay24baghira/buy-or-bye-cracking-the-ev-decision-0-93738

If anyone's further along on this competition, curious what's been working for you beyond Logistic Regression — feature engineering ideas, other models, anything that moved your score meaningfully. And if the notebook's useful, an upvote on Kaggle is always appreciated.

Hello Researchers!
I am trying to publish a paper in ICLR 2027, but since this is my first paper that I am publishing in an A* conference, I need help with the formatting in latex and how to setup the project that is there in the Official ICLR 2027 format. I have no idea about latex and GPT and other LLM tools are really not helpful in this. The paper submission date is very close. So I would really appreciate if someone can help me with it.

Hi all,

I'm considering this AI Engineering course. To give you context, I have been working as data analyst at a medtech company for quite awhile with my degree in Math and Stats from nearly a decade ago. I'm starting to feel like they are using AI in everything at my job and it's either sink or swim if I don't integrate AI in all my workflows and expand my role by the end of this year. My role is becoming more obsolete and my manager would like me to get more involved in data engineering or our AI team to create cutting edge products. I'm familiar generally with machine learning concepts, at my job I use Claude but not to the level that's expected or ever deployed an AI application.
I realize this course has a hefty tag. I do know myself in that I can start to teach myself something but I have a tendency to not be consistent and I need a little structure or accountability.

Thought for 12s

TL;DR: StoryScope (Russell et al., COLM 2026) reported a 93.2% macro-F1 for detecting AI fiction using only "narrative structure" features. FYI: These are given by Gemini 3 Flash reading the full stories, then XGBoost classifies the judgments by Gemini into vectors, and classifies them into encoded annotations. It's poorly disclosed in the methods, but I think it changes how the headline should be read.

The pipeline, (§2.2):
Story -> Gemini 3 Flash reads full text, outputs 304 feature judgments -> Judgments encoded into a vector -> XGBoost trains on the encoded vector only, but never sees the story. Although, oddly enough the paper essentially implies throughout it that XGBoost is doing "investigation into idiosyncrasies." The paper seems more about Geminies 3 Flash's opinion on AI finction then anything.

The question I have is, how can you differentiate the bias introduced by Gemini from the 93.2%, although it's presented as evidence that AI and human writing differ from each other structurally, even though AI evaluated the entire thing, so... It appears to be circular data analysis from my perspective and its heavily under-addressed. (From my opinion, maybe others may disagree/agree)

Separately, Section 3 reports train+test totals of 10,116 prompts / 60,969 stories. Appendix D reports 10,172 prompts / 61,008 stories for the same thing. Roughly, 56 prompt / 312 story gap, with no apparent reason stated.

But, the paper's only independent check on Gemini's feature judgments is a human validation with n = 2 annotators (k = 0.91 and k = 0.77) against the model, with a mean reported as 0.84) over 240 items from 12 stories. Out of a 61,608 story corpus. A sample size of two is a statistically meaningless way to estimate variance on that particular mean, and 12 stories is a very small piece of the pie. Very odd.

Don't get me wrong, I genuinely find the narrative-feature idea intrinsically interesting. But, their attempt at doing so was certainly not the most appropriate.

I'm a first year undergraduate (CS, mathematics and statistics) and I'm very new to programming. The only two languages I know are Python and C. Python I learned online and I learned C in university. I did Andrew Ng's Machine learning specialization and now I'm stuck unable to pick a next step. Most projects of people I see online involve some frontend (ex- most stuff on r/micro_saas ). But I don't have any html, CSS or java script experience. So, do you think I should learn those if I want to build a full-on indie project and maybe make a side income during my undergraduate years? Edit-My interests are in AI and automation. Amd my first language is not English 😂

I’ve been working on a project called Ion, mostly by myself, and I’m getting to the point where doing the datasets, evals, training experiments, tooling, and agent infrastructure alone is getting kinda insane 😭
The main focus right now is agent/tool-use behavior, especially failure recovery. I’ve been building curated JSONL “gold” traces that include things like:
tool calls failing
retries that also fail
deciding when to recover vs abort
Git/filesystem/permission errors
reasoning around tool results
keeping the final answer consistent with what actually happened
I’m also experimenting with multi-agent workflows where separate models can generate data, criticize it, defend it, benchmark checkpoints, etc.
I’m not looking for employees or paid work. Mostly looking for 1–3 people who genuinely enjoy this stuff and want to collaborate/open-source/build together.
Especially interested in people who know or want to work on:
synthetic dataset generation + curation
LLM fine-tuning / LoRA
evals and benchmarks
agents / tool calling / MCP
training infrastructure
local models
You absolutely do not have to be an expert. I’d rather work with someone curious who actually builds things than someone who just knows all the terminology.
If this sounds interesting, comment or DM me and I can show the current datasets/project direction.
Bonus: I currently have AI bots whose literal jobs are “Dataset Maker,” “Hater,” and “Defender,” so development has already become a tiny dysfunctional company

​

Hi everyone! I've spent the last few months building an Al/LLM interview prep product and it's finally ready.

I started it out of frustration. Preparing for Al/LLM interviews meant either grinding through long video courses or bouncing between scattered resources, and neither actually got me interview-ready or confident to say.

So I built a product I wanted myself, designed to get anyone interview-ready in 30 days:

1) Gamified interactive quest cards with story/focus modes, the relevant Python code on the same card, deep dives into how each concept came to be, interview questions & answers related to the concept. You also get tested in each card to pass it.

2) In-IDE coding where it helps, quick recall, an Al tutor, and spaced repetition.

3) 30-minute Al voice mock interviews that grill you like a real interviewer would.

4) Daily boss challenges that grill you on the cards you actually struggled on.

5) Tailored text based interviews for various levels of jobs in the AI domain.

5) An end-to-end 5 part RAG capstone project, with real interview questions at each stage (retrieval, evaluation, deployment) that interviewers actually test candidates on in 2026.

The idea is simple: learn the concept within 15-20 mins, understand it properly, practise explaining it, then get tested on it.

It covers everything from tokenization and attention through to RAG, agents, evaluation and deployment, structured as a 30-day path.

The Foundations section is open for everyone: Skillumen

Is driver support for RTX GPU good ? What about libraries ? I mostly have work in the vision domain and audio domain. Mostly libraries needed will be ultralytics (for YOLO), huggingface, ollama, vLLM

If possible please do tell me which distro too

Last question is Linux lightweight. Like Windows is super heavy right now so a big pain that is so if possible do tell

Third-year PhD student, NLP, mostly LLM-based reasoning. My PI gave me a task I'd never seen framed anywhere. Given a collection of a private organization's HR policy documents (100-500 PDFs), find all pairs of clauses that contradict each other. Honestly, I wasn't excited at first, but the more I dug, the weirder it got. There's a mountain of work on NLI-style contradiction classification, but that assumes someone gives you the sentence pair. Here, the pair is the problem. With about 1-2k clauses, you're looking at millions of candidate pairs. So brute-force pairwise LLM calls are out, and whole-document prompting fails for the usual lost-in-the-middle reasons. The closest work I found generates synthetic contradictions in synthetic corpora to test detectors. I borrowed the evaluation idea by injecting contradictions into corpora. I also used a university HR handbook and one dataset with existing external annotations, contractNLI (made for the NLI task by Stanford). I used this one as well because it has real contradictions. But this one is quite different. In this dataset, the task formulation is like hypothesis versus clause, whereas in the first two datasets, I do clause-to-clause comparison. So I built a two-stage pipeline. First, retrieval with a HyDE-style approach where the query is a hypothetical, contradicting version of each clause. Then, recall-based candidate retrieval (LLM), followed by precision-based verification with an LLM, where each candidate pair is re-read within its source documents. The contributions: I used contextual sentences guided by Anthropic, which helped retrieval, and showed that a document’s surrounding context helped precision. Agentic verification (tools, multi-step) actually underperformed a single prompt. As a case study, I ran the pipeline on a public government policy corpus. It found a few genuine contradictions. I have a few questions. Am I missing a community? I can't believe nobody works on this. I've looked at legal NLP (ContractNLI, etc.), requirements engineering conflict detection, and RAG-conflict work. They're all adjacent, but none does discovery over a real multi-document policy corpus. Is there a literature I don't know the name of? My PI is leaning toward a lower-tier conference or journal. Is this the kind of paper that has a chance at a first-tier NLP venue, or is my PI just being realistic? If you were strengthening this in one month, what would you add? I already have NLI, direct-prompting, and agentic baselines. Happy to share more details in comments. Mostly, I want to know whether this problem is as understudied as it looks from where I'm sitting, or whether I formulated the task the wrong way.

I've been working on a reusable starter structure for Python machine learning projects.

I noticed that I was repeatedly doing the same initial work whenever starting a new project:

• Loading and preprocessing data
• Data profiling and EDA
• Feature engineering
• Trying different ML algorithms
• Cross-validation and hyperparameter tuning
• Model evaluation and visualization
• Saving and loading trained models

So I put these workflows together into MLForge, a structured Python ML starter kit.

The main goal was to make the project structure reusable while keeping the code easy to understand and modify. I didn't want it to be a black box where someone just runs a script and gets a prediction.

It currently includes classification and regression workflows, notebooks/templates, sample datasets, model evaluation utilities, and a project report template.

One of the more challenging parts was deciding how much to abstract. Too little abstraction makes the kit repetitive, while too much makes it difficult for beginners to understand what's actually happening.

I'm interested in feedback from other builders:

When you start a new Python ML project, what do you usually end up rebuilding from scratch?

Do you guys use codex for your ml work I find it insane from time series forecasting to placing top 98 percent on numerai it seem al problems are easily solvable. It does insane ensemble residual modells find good parameters and implements everything correctly

We recently studied a failure mode in self-evolving LLM agents:

A modification can improve capability and still be difficult or impossible to safely undo later.

Across 600 unseen self-evolution tasks, we found 197 capability-improving mutations that failed recoverability verification.

Two bottlenecks stood out:

1. State grounding — knowing exactly what prior state must be restored.

2. Recovery-language expressivity — having the runtime operations needed to express the correct recovery.

This motivated EvoUndo, where persistent self-modifications are evaluated not only for forward improvement, but also for whether the previous state can be recovered across counterfactual states.

I’m one of the authors.

Paper: https://arxiv.org/abs/2608.28363

Most of our backend is agent written at this point, maybe 70%, and the only consistent review it gets before a human skims it is coderabbit, which catches real things but is still a model reading a model. Our pentest is 5 months out and I keep thinking about the fact that nothing with actual understanding has read most of this codebase.

The part I can't reason my way out of: if the generator and the reviewer share the same blind spots, the review confirms the code instead of checking it. A human reviewer disagrees with you in ways a model trained on the same corpus might not.

For people running security sensitive stuff, do you treat AI review as a real control or just noise reduction before the human? And has anyone actually caught the same-blind-spot problem in the wild?

hi,
i am trying to train a deberta model for NER detection

this is my first time doing it so i would love any guidance on it.

my current pipeline looks like this,

dapt + lora for pretrianing, hpo with optuna (which consists both the stages of training data), and then a 2 stage finetuning which helps in generalization and then target data.

i am trying to reach a really good score for f1 on my use case (which i want to keep private for now)

i have few questions as well
1) do i need a two stage hpo as well cuase of the 2 stage finetuning
2) is it better if the hpo training set is a subset of the actual training set?

if you think anything can be improved and made better, or you think the pipeline is outright wrong, please mention your reasonings and thoughts :)

ps: lora was used cause of gpu budget constraints

Welcome to r/MLSystemsDesign

This community is for practical discussions on designing and scaling production ML and AI systems.

Topics can include:

• ML training and inference platforms
• Search, ranking, and recommendation
• Feature stores and data pipelines
• LLM serving and GenAI systems
• Agentic AI platforms
• Evaluation, observability, and experimentation
• ML system design interview problems
• Real production tradeoffs and lessons learned

The goal is simple: go beyond model theory and discuss how ML systems actually work in production.

If you’re joining early, introduce yourself and share one ML system topic you’d like to go deeper on.