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.

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.

Hey everyone,

I’ve been working on a learning platform focused on helping beginners actually understand machine learning instead of just running library code.

A lot of people (including me when I started) jump straight into scikit-learn or tutorials and later realize they don’t really understand why the model works or fails. So I tried building something different:

• Math and Python are connected side by side
• Interactive playgrounds (you can drag lines, change parameters, and see what happens)
• Lessons follow a clear structure: Problem → Intuition → Math → Code → Where it goes wrong
• Everything runs in the browser (no install needed)

There’s a free section available right now that covers the foundations (including an interactive linear regression demo and some Python essentials).

You can check it here: https://mlmastery.io

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!

I'm running a machine learning model on the telecom IBM dataset. Now I have split. I have done the test train split, and I have to now see whether I want to scale or encode. So for that fact, I have my Vadar compound, which is basically NLP , natural language processing, that I have done, and I got values between -1 and 1. [basically its cutsom customer feedbcak column that was added]

And then another column i feature engineered column is num streaming, which is basically a combination of two of the streaming columns. So zero means they don't have any streaming column; one means they have one of the streaming column; two means they have two of the streaming columns. Since this is like numerical, you know, zero, one, two, obviously, so I made it like a numerical column. And then the second column is num minor protect, which is basically a combination of online backup and device protection. Zero means they have none of the above; one means they have one of the service; two means they have two of the services. So here's the thing. I don't know, since they all are between the range of -1 to 1, zero to 2, zero to 2. Since I am planning to run Lasso, logistic regression, XGBoost, and Random Forest, I'm very, very confused on whether do I have to scale this or not scale it. I really don't know. But they all are kind of, like, in a similar format, like, you know, it's - 1 to 1 and zero to 2, zero to 2. They all are kind of in a similar format. Do I scale them? And if I want to keep my outliers in Vadar is it ok to normalize

I'm weighing a single-engine retrieval setup for local RAG against keeping separate keyword and vector services. Exact function names and version strings matter alongside semantic matches.

For vector databases like Milvus, BM25 and dense search can share a schema and use rank fusion in one request. But the comparison I am considering also flags a gap: typo-tolerant BM25 search is still different from fuzzy filtering or a synonym list.

I would probably test misspelled queries before making the infrastructure decision. The published Docker walkthrough uses roughly 98K records and equal CPU and memory caps; its timings are explicitly directional, not a production benchmark.

For a documentation assistant, what failure rate on misspellings would justify retaining a separate keyword engine instead of handling known aliases during ingestion?

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’m looking for experienced AI researchers and AI engineers who have been working in the field for a while, have solid hands-on experience, and, ideally, earn a good salary. :)

What books, research papers, articles, or other resources would you recommend for someone who wants to dive deep into AI through self-study?

I think this will be useful for those learning Transformers architecture

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 ?

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.)

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.

It's just a casual implementation, built from scratch in PyTorch, focusing on grammatical gender agreement and custom tokenizers (with reference tokens) under the hood. It addresses a common issue where standard English-Polish translators struggle with speaker/listener gender context, managing it here via prefix tokens in the decoder. Feel free to share your thoughts.

NOTE: This is a personal educational project and a proof of concept, not a production-ready tool.

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

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

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!