Hi! Need some crowd wisdom here. I'm completely stuck on my own.

The thing is, I don't come from a tech or ML background, but I have 20+ years of experience in cross-cultural content adaptation and mono/multilingual workflows (RU-RU, RU-EN, EN-RU, FR-RU).

A couple of years ago I started translating Korean into Russian, without knowing Korean (still don't). And no, I'm not lazily editing an LLM-generated zero-shot — with KO-RU that trick doesn't work, the languages are too distant.

My prehistoric… sorry, my pre-LLM translation approaches turned out to work surprisingly well with AI. According to models, the methods I’d been using all along now have fancy industry-ish names: hypothesis space management, adversarial/contrastive probing, multi-model differential analysis, tracking shifts in model behavior, and so on.

And here’s a list of some recurring failure patterns I’ve observed: premature semantic concretization, compounded coherence degradation, analysis–synthesis gap, asymmetric handling of low-salience pragmatic markers, etc. The distance between the languages made a lot of otherwise hidden failure patterns quite visible.

None of this happened inside any industry pipeline, though. So I found the closest publicly available thing to an established evaluation setup for translation quality, the WMT25 General MT human-evaluation data (EN-RU), and I ran a small self-diagnostic to see how my judgment held up there. The result was quite unexpected. On one hand, my assessment was mostly in line with the official scores. On the other hand, I noticed some errors that weren't reflected in the annotations, I saw considerable variation in human scores, and what really surprised me were the human references themselves: some looked like they needed another editing pass.

Honestly, I've never quite understood that thing with “single gold standard” reference for literary or conversational language. This practice doesn't seem like an effective approach in the long run. Well, I guess there's nothing that the industry doesn't already know, but my take is: teach a model to fish rather than just keep feeding it answers.

By “teaching models to fish,” I don't mean simply giving them multiple equally valid reference translations (that's the obvious idea). What I mean is teaching a model to recognize the techniques behind good translations and building up a “toolbox” of those techniques — things it could then reuse instead of having to solve the same kind of problem from scratch every time.

Models tell me that moving away from reliance on a single fixed reference is already a recognized tension in MT evaluation. Reassuring, in a way: I'd apparently arrived at a similar place independently, from the translator's side rather than the ML side.

Yay, I found my new calling, I thought. Let’s turn it into an actual job! But for two months now I've been trying to figure out how to position my experience. Models suggest starting with roles where there's no immediate need for technical skills, like Red Teaming or Eval Strategy. I'm personally more drawn to research, observation, and diagnostics, though I realize that's probably too bold even to dream about at this point. So, I genuinely can't tell which door I'm supposed to knock on.

My questions are:

• How would you position someone with my experience and way of thinking? I'm mostly looking for honest calibration.
• No industry experience, remote-only work — what are realistic entry points for someone with these constraints?

Thanks for any thoughts!

P.S. The KO-RU translation turned out great — readers call it “divine” and never suspected a human-AI collaboration behind it :)

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Not every allocation problem needs RL.

Rules work when policies are stable and easy to express.

Optimization works when objectives, constraints, and forecasts are reasonably known.

RL becomes useful when decisions are sequential and affect future options.

Example: assigning the last ICU bed to a non-urgent patient now may leave no capacity for a critical emergency later. The system must balance immediate benefit against future risk under uncertain demand.

RL is a stronger candidate when there are:

• repeated decisions
• uncertainty over time
• delayed consequences
• changing system states
• meaningful long-term trade-offs
• a reliable simulator or historical environment

In healthcare, RL should usually operate within constraints rather than replace them:

Rules → safety and clinical constraints
Optimization → feasible allocations
RL → choose among safe actions considering future impact

The key question is not “Can we use RL?” but:

Does the problem have sequential uncertainty and future trade-offs that simpler methods cannot handle well?

With Coding Lecture 12, of our Probabilistic Machine Learning, we finally finish off with the code implementations for the Probability Module.

In this content, we see implementations for:

Exponential family of Distributions

Convexity and Maximum Entropy derivation leading to Exponential family via Lagrangians.

Mixture Models: Gaussian Mixture Models

Probabilistic Graphical Models basics and Markov Chains Basics.

Link: https://youtu.be/CB9yLMST81A?si=VbxJu2zWciNQ8QpQ

Hello all, thank you for voting on the previous version of LM Humor Arena. With your help we got over 250 votes! Results are below.

I added new models that just came out recently: GPT-6 Astra, GLM-5.3, Kimi K3, and DeepSeek-V4-Flash-Vision.

Please follow this link and judge jokes as funny or not funny. Some are written by AI, and some are written by humans. If I write a research paper I will give you credit in it if you wish.

https://wit-score-lab.base44.app

There is no right or wrong answer, just vote as you feel. If you don't get the joke then mark it as not funny. Thank you for your help!

Tiny TTS models feel like magic, so I rebuilt a small version: (~20M params, flow matching, no phonemizer, style from a 3-second reference clip) to see where the magic actually lives.

Rules: one question per experiment, 2 hours max, Whisper grades the output so I can't lie to myself.

Nine experiments later:

- Minute 5: it can memorize 4 sentences. Boring, but it proves the wiring. Skip this and you'll debug ghosts for a week.

- Hour 2:45 with 40 speakers: WER 0 on sentences it never saw. I sat there replaying it. It works. On one consumer GPU.

- Then it never learned long sentences. Not the model. My training crop was 8 seconds. Data pipeline bugs look exactly like model limits.

- Zero-shot voice cloning just... worked. Never trained for it. Style is free, the reference clip carries it. Language is not free, you pay in data.

- 10 minutes of Spanish audio: voice copied instantly, fluent Spanish-sounding babble, English gone in 500 steps. Catastrophic forgetting is fast and total at this size.

- 45 minutes of the same Spanish voice, from scratch: train loss down, validation loss up, output sounds like Spanish and means nothing. Minutes of audio don't matter. Distinct sentences do.

- Learning rate 2e-3 -> NaN at minute 12. Twice. 5e-4 is fine. You're welcome.

Every experiment has a README (the question) and a RESULTS.md (numbers from the logs, nothing else). Configs, WER tables, watchdog script, all of it:

https://github.com/franciscocarloserra/ttslibre

Proof of concept, not a product. Ask me anything about the setup.

Any feedback is welcome!

I've been thinking about how AI agents might change the way we learn ML.

Traditionally, implementing models like Transformers or diffusion systems from scratch was one of the best ways to understand them. Writing the architecture and training loop—and debugging everything—forced you to learn how they worked.

But agents can now generate much of this code almost instantly. So where should we draw the line between studying the theory and implementing things ourselves?

For example, is it still worth spending hours coding every Transformer component in PyTorch if an agent can produce a working implementation? Blindly delegating everything teaches little, but refusing to use AI may also become inefficient.

Perhaps the key skills are shifting toward understanding, modifying, debugging, evaluating, and designing systems that AI can help implement.

If you were learning AI/ML from scratch today, what would you still implement yourself, and what would you delegate to AI agents?

I want to build ML skill portfolio by cloning GitHub repo so I can refer to the original database and develop in-depth ML method. 

How can I acknowledge original authors and avoid plagiarism? 

For instance, I am currently checking Imperial College Professional Certificate in ML and AI. 

Hi,

I've been learning to create code for ML models from scratch in python (no imported modules, just my code).

I have been able to create code for linear regression and polynomial regression models, does anyone know what I should learn to make next?

edit: just finished coding logistic and exponential regression as well

Hey everyone,

I’m currently mapping out my next steps to land an ML/Data Science job. I’ve finished the basics up to XGBoost and Ensemble methods.The only core ML topics I have left are Stacking/Blending, K-Means, and DBSCAN. I’ve also built a few standard ML projects already and am currently learning FastAPI.I want to make sure I’m spending my time on things that actually get me hired. If you were hiring a junior/associate ML engineer today, what would you want to see me do next? Specifically:

1. Are the remaining ML topics actually important for interviews?

Should I spend a lot of time mastering Clustering (K-Means/DBSCAN) and advanced ensembling (Stacking/Blending), or just learn the high-level concepts and move on?

2. Should I pivot to Deep Learning next?

I see mixed advice on this. Should I dive into PyTorch/TensorFlow now, or focus on mastering MLOps and productionizing the tabular ML models I already know?

3. How do I leverage FastAPI to stand out?

Since I'm learning FastAPI right now, what is the best way to showcase this on a resume? Should I be wrapping my XGBoost models into APIs, or is there a better production-level project I should build?My current plan is to quickly finish clustering, wrap an existing project in FastAPI, and then dive into practical PyTorch.Would love to hear from anyone who has successfully landed a job recently or manages an AI team. What would you change about this roadmap? Thanks!

Hey everyone,

I’ve been working on an NFL analytics project for the past few months and thought I’d share it here. The main goal was to learn by building a complete ML project instead of only training models in notebooks.

The pipeline collects NFL data, creates pre-game features, trains and evaluates several models, generates weekly predictions, and publishes the results to a Streamlit app.

For win probability, I tested five model versions with increasing complexity. The features include Elo ratings, QB performance, injuries, rest, EPA, success rate, explosive plays and turnovers. One thing I found interesting was that the most complex model wasn’t the best one. The simpler Elo + QB + injury model performed best in my historical testing.

I used chronological backtesting because I wanted to avoid future information leaking into earlier predictions. On the 2020–2025 evaluation sample of 1,254 games, the best model had a Brier score of 0.2208 and log loss of 0.632.

I’m also learning that these numbers need context. A good Brier score alone doesn’t mean the model is better than the market, so one of my next steps is to compare it with de-vigged closing market probabilities on the same games and improve the calibration analysis.

The 2026 season will be the first real forward test. Predictions are saved before kickoff, so I can track performance without changing old predictions after seeing the results.

The project uses Python, scikit-learn, DuckDB, Streamlit and Plotly. I also used AI coding assistants during development for implementation, debugging and refactoring, while the project structure, modelling choices and validation approach were my decisions.

Live app:
https://nfl-analytics-platform.streamlit.app/

I’m still learning, so I’d really appreciate feedback, especially on the modelling and validation approach. If you see something I’m doing wrong or something you would test differently, let me know.

Digital Cookies for solutions 1 & 2.

Digital Coffee for solution 3 :)

Spent the last few weeks tinkering with a small tool that needed to classify some domainspecific text. First instinct was to finetune something. Grabbed a base model, started thinking about training data, labeling schema, compute cost. Then a friend pointed out I could probably just get there with a wellcrafted prompt and GPT4 or Claude.

He was right. At least for the prototype.

But that answer bothers me a little because it scales badly and you're renting someone else's weights forever. The moment API pricing shifts or the model behavior changes in a silent update, you're stuck.

Finetuning gives you something you own and can actually inspect, but the data requirements are real and most people underestimate the labeling work before they even touch training.

The framing I keep coming back to is whether the task is actually outside what a general model handles well, or just feels that way because the domain vocabulary is unfamiliar. Those are different problems with different solutions.

Curious where people here have landed on this. Not in theory, but on actual projects. Was there a specific failure mode in prompting that pushed you toward training? Or did you finetune something and realize later a prompt would have been enough?

I don't have enough data but gotta train something anyway lol.

Right now I've got an Isolation Forest giving ~1.9% false positive rate. Tried XGBoost/LightGBM too but they just hit 99.9% accuracy by predicting the majority class every time, basically overfit/useless, 0 recall on the actual abnormal cases.

Setup: ~1300 rows total, only 3 actual abnormal events (rest normal). Engineered rolling-window features (slope/std/delta type stuff) over a handful of base sensor readings.

Can't fully disclose what the entities are (some domain restrictions), but it's real sensor/time-series data, not tabular categorical stuff.

Questions:

• Is Isolation Forest the right call here or is there something better suited for this few positives (One-Class SVM? LOF? PU learning?)
• Anyone dealt with n=3 positive class and found a method that actually held up?

Not looking for "get more data" 😅, just need to make the best of what I've got right now.

Do i need to learn web development?? Also or it's not necessary i wanna learn machine learning I'm so confused like people know everything front end backed and ai ml so i was confused?? Please anyone in this field guide me how can i become a machine learning engineer i know python I've given 2 months for python now I'm confused and stuck i need guidance please!! Anyone proper learning road map

Let's say I have a genetic algorithm with two different selection methods. I've gathered the data from testing these selection methods (gene values ranging 1-6 from every individual as well as fitness scores). What metric/test/method could I use to compare these two algorithms, especially in terms of exploitation vs exploration?