My model was giving me suspiciously good results.

Cross-validation looked completely fine. The problem? My pipeline was leaking information the model should never have seen.

I fixed the leak and reran everything. The results changed dramatically.

Full experiment + what went wrong:

https://towardsdatascience.com/my-machine-learning-pipeline-cheated-and-cross-validation-never-noticed/

The scary part wasn't the leakage. It was how

trustworthy the original results looked.

Packaged an ML model as an API in Module 5 of #mlzoomcamp.

✅ Checked the pinned model artifact
✅ Served predictions with FastAPI
✅ Built and tested the Docker image

My repo: https://github.com/ashuzzz151/machine-learning-zoomcamp-homework/tree/main/05-deployment

OpenAI just announced three new math and physics results produced by its models, all machine-checked in Lean. They call the manuscripts "a form of raw research data," and they've asked outside experts to organize groups "to understand the new results." So the results exist and they've been checked (maybe, sort of), but nobody understands them yet.

I think this matters for anyone learning ML right now, because a lot of people here will likely ask, with reason: if a model can write the code or prove the statement, why should I spend my time learning how it works?

My answer: understanding is what lets you build on things. Once you really get backprop, or attention, or why a loss is shaped the way it is, it becomes one step you can use without thinking inside something bigger. A result you can only take on faith is a dead end. You can use it (maybe, sometimes), but you can't extend it or fix it.

Science compounds through understanding, and so does your own learning.

Thurston (Fields medalist) argued that the point of mathematics is human understanding, not producing theorems. I think that's even more true once theorems get cheap.

I wrote a longer essay on this, using OpenAI's earlier Navier–Stokes result as the main example: https://lacuna.tiptreesystems.com/paper/ai-science-vs-human-science/art_3901430d6c0e4bddb284362100ef8548

(Disclosure: I'm a postdoc at Mila and a cofounder of Tiptree, which builds AI tools for researchers. So I'm not arguing against AI doing science. I'm arguing about cultural incentives, mostly, and opining about history.)

I’ve been thinking about a new workflow to save time: instead of sitting through hour-long educational YouTube videos, I want to extract the transcript/subtitles and feed them directly into Claude.

I have two main questions for anyone who has tried this:

1. Does anyone have a solid prompt to make Claude act like a teacher? I don't just want a dry, bulleted summary of the video. I want Claude to actively guide me, quiz me on the material, and break down complex concepts from the text as if it were tutoring me 1-on-1.

2. Is this actually an effective way to study, and what will I miss? Obviously, I'm saving a ton of time, but I'm worried about the trade-offs. For those who do this, how much does the lack of visual context (slides, code on screen, whiteboard drawings) or the instructor's vocal emphasis impact your learning?

Has anyone used this method long-term? What are the biggest pros and cons, and how do you structure your prompts to get the best learning experience?

I evaluated a classifier with ROC AUC, precision, recall, and F1, then used
cross-validation to measure score variation and tune a parameter.

My repo: https://github.com/ashuzzz151/machine-learning-zoomcamp-homework/blob/main/04-evaluation%20metrics%20for%20classification/homework.ipynb

#mlzoomcamp @Alexey Grigorev

An O'Reilly survey of 209 security professionals ranked employees pasting sensitive company data into consumer AI tools as their top AI security concern — above AI-powered attacks, above AI-generated phishing.

The structural problem: when staff route proprietary data through tools outside IT visibility, there is no audit trail and no record of where that data went. The data has already left before anyone notices.

What makes this harder than traditional shadow IT is the silence. No unusual login. No bulk download flag. An employee pastes a contract, a customer list, or internal financial projections into a chatbot and the activity looks identical to normal browser traffic. The 209 security professionals in this survey ranked it above the threat categories their teams have spent years building controls for.

For those dealing with this in practice: how are you actually handling it? Not the policy-document answer — the real operational one, given that banning AI tools entirely means losing the productivity argument to every business unit that pushes back.

It's an accelerated program so I'm looking at 8 classes or 6 classes with a thesis.

It's also at undergrad rates.

Also, my company does $5,250 of tuition reimbursement a year, so if I take 3 semesters to do this, that'll be $10,500, and the Masters in total will likely be around $12,000-14,000.

ALSO, I didn't get an internship. I graduate in 2 months with a BS in CS. Doing this would give me another shot at internships through the college.

They have other emphasis areas like Cybersec, Data science, and two others, but AI is the one I'm most interested in.

I'm wondering about people's experiences here. I'm also a people's person, so if a Masters helps me into a management position one day, cool with me. Looking at this from a lifelong investment angle too.

My kids asked me how AI “thinks”...

I wanted an explanation they could play with. I remembered seeing a cool demo where you draw a digit and watch a neural network recognise it in real time.

I tracked it down on Brilliant, but the version I found was behind a paywall. So I built my own, with a simple convolutional neural network (CNN), already trained on handwritten digits, and a view of what happens inside.

Draw a 1 -> Add a couple of strokes to make a 4 -> Slowly close the top into a 9.
Watch the patterns inside the network and its confidence change with each stroke.

My kids loved it. Share it with yours :-)
Demo: https://hasin.eu/projects/cnn-digit-recognition-in-action/

#MachineLearning #LearningByDoing

Four years ago I posted a first version of the Mechanical Neural Network, a mechanical model of a fully connected neural network explaining structure and functionality of artificial neural networks.

Now a new 3D printed version exists along together with a series of teaching videos. Check out these videos here. For now this series covers structure and functionality of ANNs, gradient descent and backpropagation, as well as learning rate and momentum. The videos are in German but with YouTube auto translate and subtitles this should hopefully be not a big hindrance.

There is also a short explanation video on what the Mechanical Neural Network actually is and how it can be used in teaching.

I think this will be useful for those learning Transformers architecture

Hey everyone!

I'm a BTech AIML student currently learning Machine Learning and working towards becoming an ML Engineer.

So far, I've learned Linear Regression, Gradient Descent and its types (Batch, Stochastic, and Mini-batch), Logistic Regression, and evaluation metrics like Precision, Recall, and F1-score. I've been learning the concepts practically using Python and Scikit-learn.

Now, I'm confused about what to do next.

Should I start building projects with the concepts I've learned so far?

What beginner-friendly projects would help me understand ML practically?

Should I learn more algorithms first, such as Decision Trees, Random Forest, SVM, and KNN?

What skills should I focus on to become internship-ready?

How can I make my projects stand out when applying for ML internships?

I'd appreciate advice from people working in ML or those who have been through a similar learning journey. I want to build real-world projects and develop practical skills rather than just follow tutorials.

Turning a PDF into clean Markdown sounds like an OCR problem until you try it on real documents. Two-column papers, tables that run across pages, a formula sitting in the middle of a sentence. Our team has been training a small model for exactly this for a while, and we just released the code and weights, so here is what we learned along the way.

The architecture split mattered more than we expected. Layout analysis is its own model, and you can swap it for a different one. Recognition and reading order are predicted together in one pass of the main model, which removes a whole sequential step. That split, together with how we post-trained it, is where most of the accuracy and speed came from.

For post-training we used a loop we call RSI. The model predicts on a large batch of documents, and each sample is sorted as correct, wrong or uncertain (uncertain means repeated predictions disagree). A VLM writes down the reasons behind the wrong and uncertain ones, the reasons get grouped into clusters, and each cluster becomes the plan for the next round of targeted training data. We tracked progress by the repair rate of each cluster in the following round, which told us much more than watching one benchmark number.

Where it landed: in our comparison against published scores it came out first on olmOCR-Bench, OmniDocBench and DocParsingBench, and it was the fastest of the 21 models we timed on the same 100 pages. Multi-column pages are still a weaker spot (4th in that olmOCR-Bench category), and running it locally takes an NVIDIA GPU with 24 GB of VRAM.

The model is OXR, from SoMarkAI. Repo, weights and the full benchmark report are in the first comment. Happy to go into detail on the training loop, it was the most interesting part to build.

I've been working on a generator of synthetic URLs for training and testing phishing detectors, and I've released it with its full evaluation: https://github.com/youssefismail-dev/limnex-uie (Apache-2.0, code and weights).

The motivation is a data problem. Phishing URLs are scarce, feeds only contain what was caught and reported, a URL is usually dead within days, and the data is awkward to share. Limnex UIE is a 25.5M-parameter character-level transformer, trained from scratch on Common Crawl URLs and the OpenPhish community feed. It doesn't use a pretrained model, word lists or templates. The final model is small enough that training it takes only about 3.5 hours on a free Kaggle T4, so anyone can retrain it. Getting there took a few months and dozens of pre-registered experiments.

I think the evaluation is the more interesting part, so most of this post is about that.

Realism is graded one URL field at a time. For each field (label, suffix, subdomains, path, query) and each class, a classifier tries to separate generated strings from held-out real ones. Two real samples don't score an AUC of 0.5 against each other, so the score is the excess AUC over a real-vs-real floor, measured over 5 independent generations, and a cell passes only if that excess is shown to be below 0.05. 29 of 32 cells pass. The ones that don't are written up in detail: phishing query strings in free mode are still detectable, and so is the vocabulary. A detector that simply checks whether each word appears in real data separates whole URLs quite easily (an excess of about +0.16), even though character-level detectors don't. In a small human test, ten people got 55 of 100 right, which says little either way.

I also compared it with simpler generators trained on the same rows and passed through the same filter: a 5-gram model, a 2x512 char-LSTM, Qwen2.5-7B-Instruct prompted with 20 real examples per request, and rule-based look-alike domains (typos and character swaps, in the style of the dnstwist tool). UIE was more realistic in 28 of 31 comparisons, with 3 ties and none where a baseline came out ahead. The LLM was the least realistic of the learned generators: its URLs look tidy to a person, but they're stereotyped, with few suffixes and almost no query strings. One caveat is that UIE was developed against this test. A word-level detector built after the model was frozen gives the same ranking, which helps, but it isn't a complete answer.

The question I cared about most was whether the data actually helps detectors, so I pre-registered it. A char CNN and a hashed n-gram logistic regression were trained on 1k, 10k and 100k real URLs per class, with and without synthetic URLs, and tested once on phishing that appeared after UIE's training data ended. Adding UIE's URLs helped in all 6 settings, by 2.7 to 24 points of recall at 1% FPR, most when real data was scarce. Helping more than the best simpler generator held in only 1 of 6, though. UIE was ahead in 5 of 6, but by small margins, and on PhreshPhish the LLM's URLs helped more in 4 of 6 despite being the least realistic. So realism and usefulness turned out to be different things, which I didn't expect going in.

As a case study, I retrained two members of my own phishing detector (about 1M real training URLs) with UIE's data, and recall at 1% FPR on its frozen external test went from 0.272 to 0.544 (95% CI +0.15 to +0.41). That number needs its caveats: it's one benchmark and one seed, a large part of the gain comes from one phishing family (look-alike pages on Blogger's country domains), and the retrained models got slightly worse on the detector's older validation data.

On safety: it only writes URL strings, with no pages, lures or DNS lookups. Every generation has to pass a Bloom filter of the 14.9M training-side URLs, which removes copies of real URLs (about 1 in 100 raw phishing samples is one), and no bulk generated phishing is published.

It's a v0.1.0 research release with real limits: one phishing source, URLs from 2024 to 2026, and about 2 URLs per second on a laptop CPU. I built it with Claude Code as a coding assistant, which is credited in the README. Criticism of the evaluation is the feedback I'd value most.

Hello everyone!

I’m planning to pursue a BS in Computer Science and want to build my career in Artificial Intelligence. I’ve explored basic Python, Machine Learning, and AI tools, and now I’m trying to identify the right specialization to focus on.

There are many exciting fields, including AI Engineering, Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, and LLMs. However, I’m confused about which direction offers the best opportunities and long-term growth.

I would appreciate your honest opinions on these questions:

1. Which AI specialization would you recommend for someone starting in 2026, and why?
2. Is it better to become a general AI Engineer first or specialize in NLP, Computer Vision, or Machine Learning?
3. What is the difference between AI Engineering and ML Engineering in real-world jobs?
4. Which fields have promising job opportunities for fresh graduates and entry-level candidates?
5. Is NLP/LLM development becoming oversaturated, or does it still have strong career potential?
6. Does Computer Vision offer good career opportunities compared with NLP and Generative AI?
7. What kinds of real-world projects should I build to stand out when applying for internships and jobs?
8. What would you recommend focusing on during a BSCS degree to become job-ready before graduation?
9. If you were starting your career today, which AI field would you choose for the next 3–5 years?

I’m looking for practical advice based on real industry experience, not just hype or trends. I would especially appreciate insights from working AI Engineers, ML Engineers, NLP Engineers, Computer Vision Engineers, and people who have recently entered the industry.

Feel free to share your recommendations, experiences, and any mistakes you think beginners should avoid.

Thank you! 🙌

Tags:

ArtificialIntelligence #AIEngineering #MachineLearning #DeepLearning #NLP #ComputerVision #GenerativeAI #LLMs #MLOps #BSCS #AIJobs #TechCareers #CareerAdvice #PakistanTech

I was going through the TinyTorh (it's a tool where you build PyTorch with understading how it works) exercise notebooks and landed on Autograd. Was quite overwhelmed by the code and went didn't go through it for a while. I was thinking I'll prepare for machine learning research engineer interviews and somehow landed doing tiny torch.
How relevant is all this these days, should I just start preparing on how to crack the interview than this ?

As a learning project inspired by Meta's Segment Anything, I'm experimenting with an optical illusion: a pixel X made of straight stepped blocks (image attached) whose arms appear to bend around the centre square. The effect weakens or disappears when I change the square sizes, so it depends on specific proportions.

My plan is to generate many variations, collect human ratings on whether and where each one looks curved, train a small model to predict that perceived curvature, and then test whether SAM follows the real pixels or the perceived curve.
Is this realistic for a beginner?

I'd love advice on what model to start with, how much labelled data I'd need, and how best to record where people see the curve. Any related papers or projects would also be really helpful. Thanks!