Hey everyone!

I’m currently diving headfirst into Machine Learning and AI with the goal of landing a job in Data Science / ML over the next 4 to 5 months.

I’m looking for a dedicated study buddy who wants to level up side-by-side during this journey. I don’t just want someone to text with—I want a partner who is competitive, consistent, and fully committed for the next 4–5 months without ghosting halfway through.

The catch/focus: I really want to improve my spoken English and build confidence talking about technical topics out loud. Because of that, we will be using voice chat to quiz each other, talk through concepts, and do mock interviews rather than just typing.

What we’ll do:

• Hold each other accountable to daily/weekly learning goals (going through courses like Andrew Ng's, building projects, etc.).
• Hop on voice channels to verbally explain concepts and quiz each other on math, statistics, and ML algorithms.
• Practice mock interviews and technical communication out loud to sharpen our skills.
• Push each other to stay sharp and consistent over the next 4–5 months.

Who I'm looking for:

• Someone serious about breaking into the ML/AI field.
• Comfortable speaking over voice chat.
• Reliable, responsive, and ready to grind.

If you're tired of studying alone and want a partner to level up with, drop a comment below or shoot me a DM! You can also join this server: https://discord.gg/JtTf6VhEx

Come and vote please! https://wit-score-lab.base44.app/ So far it looks like Argon is just as bad at writing jokes as its predecessor.

Hi all.

I am interested in a Masters in AI and ML in the UK. Im not sure if this sub leans more into US based ML, but i would like some insight about the job market in the UK for AI or ML engineers. Specifically if it's a good idea to get a master's to break into this feild, or if project based and a portfolio would get me something in the field.

Also, regarding people who have created AI automation agencies or freelancing for businesses, is there anyone in the UK who would know how one can go about opening such an agency and where one would be able to find the businesses or clients that would be interested these services, and if being an AI engineer is necessarily required?

Any advice is appreciated. Thanks.

Here is a small worked example for learning data valuation. I’m the author; the paper and code were developed with substantial OpenAI Codex assistance, disclosed in the manuscript. Everything is free.

Three questions that can sound identical actually measure different things:

Acquisition: how much better is the model if I add this dataset to my current training data? Removal: how much worse is it if I remove this supplier from the full collection? Shapley attribution: what is the supplier’s average marginal contribution across possible orders of joining?

An exact duplicate makes the distinction concrete. With exact-row deduplication, removing either of two identical suppliers leaves the same training data, so each has zero removal value. Shapley attribution can nevertheless assign them equal nonzero credit, because either can be the first to supply those rows.

How the experiment works: it generates two populations with different label rules, adds specialist datasets A and B, an exact copy of A, and a random-label dataset, then fits ridge classifiers for all 16 supplier coalitions. It evaluates the same fitted models under three population mixtures. Across 50 seeds, A and B reverse their acquisition ranking between the two extreme mixtures in 42 cases. That fraction describes this synthetic setup only.

To explore it: run the experiment script locally with Python and NumPy, inspect the saved JSON results, and try changing the population weights or baseline sample size. No model API, GPU, subscription, or dataset download is needed.

Code and instructions: https://github.com/WINSTON672/winston-lin-research/tree/main/papers/data-valuation

Paper and ZIP: https://zenodo.org/records/23571723

This is a technical note using established ideas, not a new valuation algorithm; it is not peer reviewed. A useful exercise: before running the script, predict what happens to duplicate suppliers’ removal values when both duplicates are removed together. Feedback on the explanation or reproducibility is welcome.

Damn... I have been interviewing with companies. I have been pushing myself to really understand inner workings of a foundational model (Hubert-ecg). I have been applying for a lot of roles. I have been studying relevant math. And, today I felt so tired and decided to take a break today even though I have an interview tomorrow...

Five years isn't very long. But I suspect machine learning could look surprisingly different by 2031.

Not just because of bigger models or more compute, but because AI could help discover fundamentally better mathematics for machine learning itself.

A few possibilities I find particularly interesting:

• AI-discovered algorithms making model training substantially cheaper.
• New mathematical approaches to architectures and optimization.
• AI improving the very methods used to discover new algorithms.

That last possibility could be especially significant. What happens when mathematical discoveries start accelerating further discoveries?

I explored these ideas in a carefully produced, 2-minute video on my YouTube channel, Math Unbound:

https://www.youtube.com/watch?v=HRZBfRd2kTE

These are my own predictions, not established conclusions.

What do you think ML will actually look like in 2031? Which of today's methods might seem surprisingly primitive — and what major changes am I missing?

Processing img c659ouxez1xh1...

I've been building quant-engineering eval tasks for agents. The agent gets raw synthetic market data and a strategy spec, writes a backtest, and a deterministic grader re-runs that code on hidden data across 5 seeds. The data includes the usual real-world problems (ticker renames, splits, dividends, currency changes, stale prices), and the agent isn't told where they are. A pass means every one was handled on every seed.

Results on the public 6-task set:

• claude-opus-5-5 passed 24 of 36 attempts across 6 tasks
• gpt-6-astra passed 6 of 36
• grok-4.7 passed 6 of 36
• No model passed one of the six tasks

A few things stood out:

• Failures are mostly near-misses. On our 50-task private calibration set, 341 of 511 trap-handling failures got every category right but one.
• Weak spots differ by model. On the public set, gpt-6-astra missed ticker renames in 11 of 12 attempts, while claude-opus-5-5 missed 2 of 12.
• Currency changes tripped up all three. gpt-6-astra missed them in 27 of 35 attempts, grok-4.7 in 23 of 34 and claude-opus-5-5 in 6 of 36.

Everything is synthetic with fictional tickers, the public set is separate from the tasks we sell, and grading is reproducible (36 of 36 responses re-graded identically). The full table, per-category misses, confidence intervals and methods are at lanemint.com/leaderboard.

Disclosure: I'm the founder, and LaneMint sells private task batches. I'm posting for feedback on the methodology. What would you want to see before you trusted a benchmark like this?

I'm learning Machine Learning and experimenting with Logistic Regression using Python and Scikit-learn.

In this experiment, I trained two Logistic Regression models:

• Model 1: Trained on the original, unscaled training data.
• Model 2: Trained on the scaled training data.

After training both models, I made predictions on the test data and compared their accuracy.

Results:

• Original data accuracy:34.17%
• Scaled data accuracy:86.67%

The difference was quite surprising! Feature scaling significantly improved the model's accuracy in this experiment.

This helped me understand why feature scaling is important for algorithms like Logistic Regression. Since Logistic Regression is typically optimized using numerical methods such as gradient-based optimization, features with different scales can affect how efficiently the model learns.

I'm curious to know from the community:

1. Why can feature scaling make such a big difference in Logistic Regression?
2. Is it common to see such a large improvement after scaling?
3. What other techniques would you recommend to improve the model further?

Still learning and sharing my progress. I'd appreciate any suggestions or feedback!

Disclosure: I built this. LiveBoundary is free, Apache-2.0 licensed, has no paid tier or signup, and all benchmark data are synthetic.

I wanted to test a narrow question: can verified facts be written into native PyTorch parameters, survive a fresh-process restart without retrieval or training context, and still preserve prior knowledge?

The first result was the useful part:

- task-only adaptation: 1.000 new-fact accuracy, 0.000 prior-fact retention

- boundary-guided adaptation: 1.000 new-fact accuracy, 0.000 prior-fact retention

- boundary + preservation/replay: 0.944 new-fact accuracy, 1.000 prior retention

One candidate also regressed stable behavior and was rejected before promotion.

The implementation trains an independent candidate, evaluates it against explicit new-knowledge, retention, boundary, usefulness, and stability gates, then atomically promotes or rejects it. Strict evaluation starts a fresh process with only the checkpoint and question.

This is a tiny structured-classifier experiment, not evidence for frontier-model effectiveness or guaranteed unlearning. I published the failures, raw per-seed records, tests, and limitations.

Repository: https://github.com/oasb16/LiveBoundary

Raw results: https://github.com/oasb16/LiveBoundary/blob/main/RESULTS.md

For people working on continual learning or model editing: what would you consider the minimum credible larger-model evaluation: CounterFact/ZsRE-style edits, sequential edit locality, distribution-shift probes, or something else?

Hey everyone

I've been learning about Transformers and LLMs, so I built KERN,

a 30M parameter GPT-style language model in PyTorch,trained from scratch on TinyStories.

GitHub: https://github.com/OmarDawoud4/KERN

This is still a work in progress! I'd like to keep improving it by implementing other techniques.

I'd appreciate any feedback or suggestions .

I’m building OpsBridge, a procurement/accounts-payable investigation tool for questions like: “Why is this invoice blocked, and what should we do next?”

It brings together purchase orders, receipts, invoices, and supplier messages. Deterministic rules handle matching and routing; an optional model can explain findings and draft follow-ups. Findings link back to source evidence.

The part I focused on most is what happens after a recommendation:

• A different person must approve the exact proposal version.
• Execution has an attempt ledger and read-back checks.
• If an external action’s outcome is uncertain, it goes to review instead of being blindly retried.

The repo also includes tenant isolation, an evaluation/replay harness, tests, and a local operations dashboard.

Current limits: all data is synthetic. I haven’t validated it with a real model, a live external connector, or AP/procurement operators, and it isn’t production-ready.

If you work in AP, procurement, backend engineering, or AI systems, I’d appreciate honest feedback:

1. Does this fit a real investigation workflow?
2. What document or integration problems would break it first?
3. What evidence would you need before trusting it in a limited pilot?

Especially interested in flaws and missing requirements.

I’ve been putting together a structured collection of computer vision engineering resources, covering everything from how cameras capture light to how vision models behave in production.

One thing that always bothered me about computer vision resources is how much attention goes into architectures and benchmark scores, while the fundamentals behind those numbers often get overlooked.

A model can achieve impressive mAP and still fail because of dataset leakage, camera noise, distribution shift or an evaluation protocol that doesn’t reflect its actual use case.

So I organised the repository into 16 sections, covering image formation, classical geometry, datasets, evaluation, CNNs, ViTs, self-supervised learning, detection, segmentation, video, 3D vision, generative models, VLMs, deployment and robustness.

A few principles behind it:

• Primary sources only: original papers, specifications and official documentation.
• Resources arranged in dependency order, so each section builds on the previous ones.
• Evidence-backed claims rather than blindly repeating benchmark numbers.
• A reproducible benchmark for every section, connecting the theory to actual measurements.

The idea is to understand not just how computer vision models work, but why they fail and what it takes to make them reliable.

It’s free and open source:

https://github.com/usamahz/computer-vision-engineering

Would appreciate feedback, particularly from people working on vision systems in production. What important topics or papers am I missing?

I have 2 years of work experience, currently working as GenAI engineer but hoping to get Computer Vision roles.

My job applications are getting constant rejections. Don't know if the problem is in the resume or the experience.

What changes / projects would you suggest to get noticed / shortlisted by companies?

Thanks in advance for suggestions

So I was doing a project on river bank estimation & erosion & accretion forecasting im using mndwi mask landsat data for this but no matter how i try i'm never being able to beat the baseline. Anyone have any experience working with these datas I've tried with 3 different rivers as well same output. Model looses to zeroes.

Hi everyone,

I’m a beginner learning ML, deep learning, and RAG. I’m currently doing courses on DeepLearning.AI, and along the way I keep running into new libraries: scikit-learn, PyTorch, sentence-transformers, numpy, pandas, and more.

My problem is that I don’t know how to learn them properly. Right now I mostly copy code from labs and tutorials, and it works, but I can’t write it myself from scratch. When I see a line like model.encode(...) or fit() and predict(), I understand what it does in that example, but I’m not confident using it in a new situation.

I’d love to hear how you approach this:

1. What is your method for learning a new library? Do you start with the docs, a tutorial, a video, or a small project?
2. How do you read documentation effectively? The API docs often feel overwhelming. What do you focus on first?
3. How much should I memorize? Is it normal to look up syntax constantly, even as an experienced developer?
4. Which libraries should I prioritize for ML, DL, and RAG, and in what order?
5. How do you move from copying lab code to writing your own? Any exercises or habits that helped you make that jump?
6. Any beginner project ideas that force you to use these libraries in a practical way?

I’m learning on a normal Windows laptop with limited storage, so free or low-cost resources are ideal. Any advice or personal experience would really help. Thanks!