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.

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.

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 ?

I think this will be useful for those learning Transformers architecture

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!

We are BS Computer Science students of the College of Computer Studies, UPHM, and we respectfully ask for your help in validating our thesis, "A Comparative Analysis of Supervised Machine Learning Models for Predicting Students' Performance in Higher Education."

Please review the paper and rate it using our short validation form (about [10 to 15] minutes): (A) methodology, (B) results, and (C) analysis code. A short summary of the study is included in the folder. Your ratings and comments are confidential and used solely for academic purposes.

📄 Paper, summary, and source codes: https://drive.google.com/drive/folders/1julvRK8DR0kNXnbkaoVYwhW9_BPdh5R9?usp=sharing

📝 Validation form: https://docs.google.com/forms/d/e/1FAIpQLSe-Mno8KoSsD78CufHU4MVDzryoKD2SWfZVFZK3Hg2DQ6N_cw/viewform

Thank you very much for your time and expertise! Sharing this post also helps a lot. 🙏

— Reynaldo B. Arandia Jr., Mark Anthony Demefeliz, Ervine U. Bose

BS Computer Science, CCS, UPHM

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.

guys want to know that where I will get campusX paid Fastapi pirated course in which he covered Render docker fast api concepts with pydantic and Redis also SQL integration

Hi everyone,

I just wanted to share an open source repo collecting 20 exploratory studies focused on Next-Concept Modeling (NCM), mechanistic interpretability (like IOI in GPT-2), and model controls.

Here is the link: https://github.com/Quantix-eng/ncm-experiment-notes

Long story short, this is an independent project documenting a bunch of empirical experiments looking into how transformers process and edit internal features. There are no claims of performance breakthroughs here. Instead, it is just honest documentation of observations alongside rigorous controls.

The repo covers NCM applied to structured tasks like Sudoku, mechanistic interpretability in GPT-2, and toy parallel models. I have included all the source code, saved results, and interpretations for every single run.

To keep things transparent, I have also categorized every outcome, including the mixed and completely negative results where things did not work as expected. AI tools helped heavily with the planning and coding phases too.

I would love to get your feedback on the setups or the interpretation limits I found. Let me know what you think if you are into transformer interpretability!

Hi everyone,

I just wanted to share an open source repo collecting 20 exploratory studies focused on Next-Concept Modeling (NCM), mechanistic interpretability (like IOI in GPT-2), and model controls.

Here is the link: https://github.com/Quantix-eng/ncm-experiment-notes

Long story short, this is an independent project documenting a bunch of empirical experiments looking into how transformers process and edit internal features. There are no claims of performance breakthroughs here. Instead, it is just honest documentation of observations alongside rigorous controls.

The repo covers NCM applied to structured tasks like Sudoku, mechanistic interpretability in GPT-2, and toy parallel models. I have included all the source code, saved results, and interpretations for every single run.

To keep things transparent, I have also categorized every outcome, including the mixed and completely negative results where things did not work as expected. AI tools helped heavily with the planning and coding phases too.

I would love to get your feedback on the setups or the interpretation limits I found. Let me know what you think if you are into transformer interpretability!

I'm an IT admin, not an ML researcher. I wanted to know whether a bounded version of ELU/SELU could train very deep networks without normalization.

The honest result: after fair tuning it matches SELU at 50 and 100 layers. One variant is better in one setting (MNIST, depth 100). Plain ELU is about as good overall. The big advantages I saw early on disappeared once every activation got its own tuned learning rate.

What I'd tell anyone trying something similar:

1. Tune the learning rate for every baseline. My early wins at a fixed learning rate were mostly a scale effect.

2. Freeze your analysis before running. I hashed the code and analysis script before every main run.

3. Use separate seeds for tuning and confirmation. Several effects disappeared on fresh seeds.

4. Check the mechanism, not just the score. A gradient profile at initialization explained why my first variant failed at depth 100 and pointed to the fix.

- Preprint: https://doi.org/10.5281/zenodo.23676200

- Code, data and kernels: https://github.com/hermi-labs/precap-activation

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.

Hi everyone,

I'm a student learning AI development, and I recently built my first RAG-based Teaching Assistant.

The project converts educational videos into transcripts, generates embeddings using BGE-M3 through Ollama, retrieves relevant transcript chunks using cosine similarity, and uses Llama 3.2 to generate answers.

I'd appreciate honest technical feedback, especially on:

1. Is my retrieval approach appropriate, or should I consider other methods?
2. How can I improve transcript chunking and embedding quality?
3. How should I evaluate whether the retrieved chunks are actually relevant?
4. How can I reduce hallucinations and keep answers grounded in lecture content?
5. What would you change in the architecture or code if you were reviewing this project?

I'm looking to learn from feedback and improve the implementation. Suggestions for specific concepts, resources, or code improvements are welcome.

Thanks!

Releasing a series of very shot notes on RL as part of my learning RL from scratch journey; its basics and upto the latest stuff with from scratch implementations and experiments in the coming days

• Explained some of the most basic and important terms when starting out learning RL with a sprinkle of math of what I found interesting
• Nice to have for refreshers/kind of first time readers too

1-page RL basics is out on my website!

Let me know your thoughts/feedback!

I'm an early-career software engineer working on production LLM inference infrastructure, primarily involving accelerator-based model serving and distributed systems.

I've been in the role for over a year, but my technical growth and contributions haven't progressed as much as I'd hoped.

I have a computer science background and generally enjoy software engineering, but I feel there's a significant gap between my current understanding and what's required to contribute independently to complex ML infrastructure.

Some of my biggest challenges:

• System architecture: Understanding how the inference stack fits together, from model execution and compilation to serving, scheduling, and hardware utilization.
• Codebase navigation: Understanding large, unfamiliar codebases without getting lost in implementation details.
• Debugging: Spending too long investigating issues without making meaningful progress.
• Performance engineering: Understanding bottlenecks, profiling workloads, and determining whether an optimization will meaningfully improve end-to-end performance.
• Foundational knowledge: Determining whether I should focus on distributed systems, ML fundamentals, compiler internals, hardware architecture, or something else.

I also recognize that I've been too passive about developing my skills, which has likely compounded these gaps.

My questions:

1. If you were in my position, what would you prioritize learning over the next 2–3 months?
2. What practical projects would help develop intuition for production inference systems?
3. How do experienced ML infrastructure engineers build a mental model of complex systems?
4. Has anyone overcome a similar knowledge gap after already spending significant time in an engineering role?

I'm particularly interested in advice from people working on production inference systems, distributed systems, or accelerator infrastructure.

I want to become a genuinely competent engineer who can independently debug problems, identify worthwhile optimizations, and deliver meaningful contributions.

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

The class modules I will have are all in the link above (in the Study Programme section). Stuff like Game AI and robotics seem to be way better on windows its making me uncertain because on the other hand macbook is way more convenient overall for everything, and our school uses google colab and whatnot I think? Our professor mentioned it once (I am a first year).

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