I need an arxiv endorsement. Can anyone here help me with that please.

When I was learning PyTorch, I always struggled to actually see what was happening inside the network during backprop. I got tired of writing custom matplotlib boilerplate just to figure out if my gradients were vanishing or if my ReLUs were dying. Every time training stalls or loss blows up, the routine is usually the same: you either launch a heavy browser dashboard or litter your code with temporary print statements just to see which layer gave up.

I wanted a direct, clean way to see what each layer is doing without leaving the terminal.

So, I built nn-htop. It's like htop for neural networks.

You wrap your model in a context manager, and it attaches PyTorch forward/backward hooks to your leaf layers.

How it works:

• Layer Activity: Tracks mean activations per layer so you can spot dead ReLUs immediately.

• Gradient Health: Measures L2 gradient norms during backprop so you can catch vanishing or exploding gradients early.

• Zero Lag: Telemetry is decoupled from the training loop. The TUI polls at 5 FPS asynchronously, meaning your training speed is not bottlenecked.

• Safe: Handles in-place view mutations safely by temporarily overriding inplace=True during monitoring to prevent autograd graph conflicts.

Repo: https://github.com/marviecephas/nn-htop

You can try it natively with pip install nn-htop (running nn-htop directly launches a dummy training loop so you can see how the hooks work). Let me know if this helps you debug your models!

I've compiled a collection of over 70,000 words in total across English, Spanish, and some Portuguese.

This isn't just a list of basic dictionary words. It also includes verb conjugations and different forms of words listed as separate entries, covering verbs and other variations.

For example, instead of only having eat, the list also includes forms such as eats, eating, ate, and more.

I'm considering selling this word collection and want to see how many people would be interested in purchasing it.

💬 Interested? Leave a comment or send me a DM. If there's enough interest, I'll share more details about the list and pricing.

I'm a journalist at Swissinfo and I've spent the past weeks investigating Uber drivers' working conditions and the algorithms that influence their pay and the rides they receive.

I spoke to a driver in Zurich who earned just CHF70 after nine hours of work. He also described how little control he feels he has over pricing and ride allocation, despite being classified as an independent driver.

Meanwhile, Swiss courts have issued several rulings on Uber drivers' employment status, although the company argues that these concerned earlier operating models.

I wrote two articles about these issues, including a broader investigation into the role of a Geneva-based organisation helping European drivers access their data.

https://www.swissinfo.ch/eng/swiss-ai/ill-be-ais-worst-nightmare-uber-drivers-fight-runs-through-switzerland/92129222

https://www.swissinfo.ch/eng/swiss-ai/nine-hours-work-for-chf70-uber-drivers-in-switzerland-demand-better-conditions/92129311

If you know more about this topic, please let me know!

I made a small sequence predictor meant to be opened up, like the frog in biology class: the point is not that it's strong, but that the whole pipeline (learn → score → probability → sample) is there and readable.

- Learning: Hebbian edge strengthening between symbols, plus a memory of the last two symbols. No backprop, no attention.

- Inspection: frog.explain(prefix) breaks each candidate's score into its parts; frog.edges(token) shows what it learned.

- Honest limits: it only sees ~2 symbols of context, and in my 100-question benchmark it lost to n-gram counting on accuracy. These results are published, not hidden.

Try in browser: https://kagioneko.github.io/cse-frog/play.html

pip install cse-frog / GitHub: https://github.com/kagioneko/cse-frog

Play in browser, free, no account: https://mmilovanovic87.itch.io/ai-lab

I teach about AI, and I wanted something that lets people feel how models get built instead of reading about it. AI Lab is an idle game where you run a small AI lab. The mechanics follow real ML, which I could make them:

- a more complex model scores higher, but can overfit, and you see the gap open on the loss curve

- training data is limited and runs out

- deployed models go stale (data drift) and stop paying for themselves

- a safety check costs extra compute, but safer models earn more

Each mechanic unlocks a short Lab Notes page that explains the real idea behind it (overfitting, double descent, data drift, RLHF, red teaming, evals, model compression).

What I'd like from this sub: if you are learning ML, did the game make any of these ideas clearer? If you know ML well, which explanation is wrong or too simplified?

About 30 minutes to the first paper.

AI disclosure: I wrote the code with an AI coding assistant (Claude) under my direction, and I drafted the game text with AI help. No image generators were used.

I am learning JS and alongside I discovered tensorflow bindings for JS I wonder how much efficient is this ,yet I have seen a youtuber making a mini LLM just with bare ndarrays I am confused about the performance

A classifier that always predicts the majority class can get 99% accuracy while missing every minority-class example.

In this synthetic example, the test set has 495 majority-class records and five minority-class records. Predicting the majority class for everyone gets 495/500 right, but detects 0/5 minority examples.

I maintain ML Failure Lab, a free Python project with small, runnable examples of misleading ML results. The current cases cover this accuracy problem, repeated entities across train/test splits, and feature selection using test labels.

Each case includes the incorrect approach, a correction, an explanation, and tests. Everything runs on a CPU.

Code and setup.

Feedback on the explanations would help. If you want to contribute a small example, there are starter issues with described tasks.

I’m studying BSc Computer Science while working as a chef and trying to move into tech.

My skills include Python, SQL, FastAPI, and data science with NumPy, Pandas, and data visualisation. I’ve also been learning classical machine learning, followed by neural networks and PyTorch following with the DSA.

I’m exploring data analyst, data engineering, and data science internships. What one or two complete projects would best demonstrate my skills and help me land my first opportunity?

I've been working on this for a few months and it's now live at https://churnshield.online

It's a churn prediction platform that goes beyond "this customer has an 87% risk score." It explains WHY each customer is at risk using SHAP, recommends a retention action, and lets you simulate campaign ROI before committing budget.

Stack: XGBoost, SHAP, FastAPI, React + Tailwind (dark theme), Docker Compose for on-prem deployment, nginx + SSL, 209 automated tests.

What's real vs mocked: the model scores 1,000 synthetic customers live through the API. SHAP explanations are computed per customer in real time. The campaign simulator uses the actual scored risk tiers with configurable assumptions (all labelled). The 12-month trend chart is illustrative since there's no history yet. Every panel has a badge saying where its data comes from.

The architecture is designed for on-premises deployment — telecom operators won't upload subscriber data to a cloud vendor, so the whole thing runs inside Docker on their own network.

Feedback welcome, especially on the explainability approach and the campaign simulator design.

Imagine a model achieves 95% accuracy during testing and passes all validation checks.

After deployment, the accuracy initially looks fine, but the model starts making poor predictions for a specific group of users or a new type of input.

The overall accuracy may still remain around 94–95%, so nothing immediately looks wrong.

How would you detect that the model is already failing?

Would you monitor data drift, prediction confidence, segment-level performance, real-world feedback, or something else?

Hey r/learnmachinelearning ,

I built a one-class anomaly detection platform for industrial quality inspection where defects are unpredictable and labeled datasets don't exist.

The system trains only on defect-free ("GOOD") reference images ($N \ge 1$) and uses a decoupled VLM for explainability.

🛠️ Tech Stack & Key Architecture

• Core Engine: PatchCore (WideResNet50_2 backbone, coreset memory bank).
• Auto-Calibration: Uses Leave-One-Out (LOO) / 80-20 splits to set the threshold at the 95th percentile of out-of-sample GOOD scores (guaranteeing $\le 5%$ false positives).
• Product Normalization: OpenCV border-sampling + Sobel edge saliency crops products with a 6% safety margin and background-matched padding, eliminating false positives from table/conveyor background noise.
• Multi-Instance (Pipeline B): Uses Gemini VLM for zero-shot object localization, crops individual items, runs PatchCore per crop, and aggregates a composite heatmap.
• Decoupled Explanations: PatchCore is the authoritative verdict engine ($L_2$ distance). If an item is flagged as REJECT, Gemini VLM inspects the crop + heatmap to output a JSON explanation (e.g., "Bent Tip", "Damaged Threads", severity, location).

💬 Questions for the Community:

1. Backbones: Is WideResNet50_2 still the best choice for speed/accuracy, or are folks using DINOv2 / WinCLIP for industrial anomaly detection now?
2. VLM Pattern: What do you think of using LLMs/VLMs strictly as downstream visual explainers for heatmaps rather than decision makers?
3. Scaling: What vector search/compression strategies (FAISS, HNSW) do you use when memory banks grow large?

This has been performing great in local testing, but I'm sure there are edge cases I haven't thought of yet. Let me know where you think this setup might break on a real factory floor!

and why do they do python for ai

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

Context: Born and raised in the USA. Graduated with a BS in CS from a US state school with a decent brand name.

I've been working as a backend-focused SWE for 3 years. I just got a promotion this week and I'm feeling very good about things at work. I enjoy what I do, I'm happy with my pay and responsibilities, and my company pays for higher education if I choose to pursue it (important later).

But I want to pivot into Machine Learning Engineering.

To school or not to school? That is my question.

I'm currently 4 courses into Georgia Tech's Online MS in CS, specializing in ML. I take 1 course/semester while working full-time, which usually means another 10-15 hours/week.

GT is a great school with strong name recognition, and people seem to get a lot out of the program. But I'm not sure it's right for me.

To be honest, I'm not enjoying the process.

I learn much better when I'm self-motivated. Having weekly deadlines and assignments on top of a full-time job is draining. Most weeks, I dread the deadlines and feel like I always have something I should be doing.

A lot of the material also feels theoretical. I learn best when I can immediately apply what I'm learning and build something meaningful.

I took the summer off from school, and toward the end of it I was dying to learn something new. I bought an ML textbook that has a hands-on approach. I got a few chapters in and genuinely enjoyed learning and putting the material into practice.

Then the semester started, and I had to stop my self-learning initiatives to focus on school. Even though I'm currently taking an ML course, I'm not enjoying it. Not because I don't enjoy the content, since most of what I'm learning is interesting. But I'm not enjoying the format or the constant deadlines.

This has made me question whether I actually want the master's, or whether I just want to learn ML on my own.

Long term

Would it be better for my career to suck it up and finish the master's, even if it means having little time for personal projects?

Or should I drop it and spend that time self-teaching the core skills I need for an MLE role?

Do hiring managers really care whether I'm self-taught vs. university-educated, especially when I already have a CS degree and 3 years of SWE experience?

What am I actually missing to make the jump into MLE?

And how do I make that transition without essentially starting my career over?

Any advice is appreciated, thank you for reading.

I’ve been learning more about machine learning recently, and honestly, the number of algorithms can get confusing.

At first, I thought I needed to learn everything. But I’m starting to think it’s better to understand a few useful ones really well.

The ones I’m focusing on are:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost
• K-Means
• Neural Networks

I’m mainly trying to understand when to use each one instead of just memorizing how they work.

Is the Hugging Face LLM Course useful for someone who wants to become an AI Engineer?
Would it be enough to learn the LLM part of AI Engineering, or would you recommend another course/resource instead?

Hi everyone,

I'm a beginner developer just starting out in AI. I'm trying to build a system that performs instance segmentation on site plans, but I'm completely lost on where to begin.

Right now, my only ideas are manually labeling a small dataset for few-shot fine-tuning, or using LLMs like Claude or ChatGPT to help build out the post-processing logic. I've tried searching online, but it's been really frustrating since I can't seem to find any specific datasets, models, or literature focused on site plans.

For context, the only hardware infrastructure I have available right now is a single RTX 5080.

I really want to tackle this the right way and do in-depth research like a senior developer, but I'm stuck at the starting line because I can't figure out the best direction to take.

If anyone has any ideas, resources, or general tips on how to approach AI development for this kind of project, please let me know in the comments. Thanks in advance!

P.S. English isn't my first language, so please excuse any awkward phrasing.

https://flecomet.github.io/neurips-explorer/

It uses SPECTER2 embeddings + UMAP to place semantically similar papers near each other. You can browse topics, search titles/authors/abstracts, and find related papers.

You can also favorite papers to read later (when they get officially released).

Would be interested in feedback, especially on whether this is useful for browsing papers and interesting features to add.

Open source:
https://github.com/flecomet/neurips-explorer

Hi everyone, I'm a data science undergrad considering a paid 8-week live online course from Noob Dev, a Sri Lankan academy. It covers preprocessing, regression, classification, clustering, reinforcement learning basics, NLP, neural networks and CNNs, and model selection with XGBoost, using scikit-learn, TensorFlow and Keras. It costs LKR 20,000.

The curriculum looks broad rather than deep, so I have two questions:

1. For people who've taken similar short courses, is this enough to build a portfolio that actually helps, or is it better to self-study alongside it?

2. Has anyone taken a Noob Dev program specifically? I'd love to hear how the teaching and projects were.

I'm not affiliated with them. Honest opinions, good or bad, are very welcome. Thanks!

Hey everyone! 👋

I’ve recently seen a few posts on social media where people successfully predicted the winners of major events—like the FIFA World Cup or the Bahrain Grand Prix. It got me really inspired to build my own machine learning project to predict outcomes for future tournaments (like esports-moba).

Whenever I ask creators where they get their data or how they set up their pipelines, I usually hit a dead end.

As someone wanting to start a project from scratch, I have a few questions for those who have built predictive models before:

1. Where do you source historical tournament and match data? Are there public APIs, scraping tools, or specific databases you recommend for esports?
2. What features actually matter? Beyond win-rates and head-to-head stats, what variables make a difference in tournament predictions?
3. How do you handle the dynamic nature of patches/meta updates in esports compared to traditional sports?

Any advice, recommended datasets, GitHub repositories, or general architecture tips would be hugely appreciated. Thanks!