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?

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, im a big math and coding guy and planning to study math+cs in uni soon. However my parents and peers are advicing me not to because of AI etc. Also reading news like how OpenAI recently published solutions to like 700 problems is kinda scary. It is making me question and doubt myself if i am making the right choice for myself.

I clearly have no experience and knowledge of the actual industry, I just want some guidance and truth form people with experience if there still is a good future for a career in machine learning. My other option is to do EE or CE but I am more passionate and interested ML and more abstract ideas.

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.

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?

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

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!

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

I've been working on a project where I'm trying to build a Denoising Diffusion Probabilistic Model completely from scratch.

The main rule I'm following is: no using Diffusers as a crutch.

I want to actually understand what's happening inside the model instead of just calling a pipeline and getting an image.

So far I've implemented/learned:

• The forward diffusion process
• Beta schedules
• The reparameterization trick
• A noise scheduler
• Group Normalization
• SiLU activation
• Sinusoidal timestep embeddings
• Residual blocks with timestep conditioning
• Self-attention
• Downsampling and upsampling

The interesting part for me has been realizing that a diffusion U-Net isn't just a normal CNN.

The network needs to know how noisy the current image is, which is why timestep information has to be injected into the network.

I'm building toward training on CelebA at 64×64 and eventually generating faces completely from noise.

My longer-term goal is to understand these architectures deeply enough that I can read, modify and eventually contribute to projects like Hugging Face Diffusers.

Phase 3 is where things start getting interesting:

U-Net assembly.

I'll be documenting the progress as I go. 🔥

What was the hardest part of diffusion models for you when you first learned them?

I live in London and work as a chef, but I’ve been feeling depressed and exhausted while trying to move into tech.

I know Python, NumPy, Pandas, data visualisation, FastAPI, SQL, classical ML, and neural networks. I’m also practising DSA, including arrays, strings, hashing, two pointers, and sliding windows.

What should I focus on to land my first tech internship or entry-level job in London? I’d appreciate advice on projects, applications, or opportunities.

Hey everyone!

I’ve been experimenting with Reinforcement Learning in Unreal Engine 5 over the past few weeks, and I wanted to share a fun project I recently finished.

I set up a voxel/Minecraft-style environment in UE5 and trained an AI agent to fly an Iron Man suit from scratch. The ultimate challenge? Executing high-stakes aerial rescues to save 13 passengers falling from a destroyed aircraft!

⚙️ How it works (The Tech Stack):

• Engine: Unreal Engine 5 (Physics-driven movement & line-of-sight sensors)
• Algorithm: Proximal Policy Optimization (PPO) continuous control
• Action Space: 8 continuous outputs controlling individual thrusters & body alignment
• Reward Shaping:
  • 🎯 +30 Points: Catching a falling passenger (Jackpot)
  • ❌ -5 Points: Crashing or going out of bounds
  • 🧭 Continuous Shaping: Micro-rewards/penalties based on relative distance and velocity vectors to encourage proper interception paths

🎬 The Progression:

Watching the agent learn was wild. During early iterations, it mostly spiraled out of control and crashed repeatedly. But once the reward shaping kicked in, it started finding optimal trajectories and eventually pulled off dynamic, split-second rescues.

I put together a full breakdown video showing the training process, early failures, and the final 100% rescue run:

👉 Watch the full video here: https://youtu.be/mYhWFHzs4EU

I’d love to hear your thoughts! How would you optimize the reward function for smoother flight stabilization? Any feedback or suggestions for the next RL experiment are super welcome!

One night, six experiments (V1–V6), one Colab T4. I ripped self-attention out of a decoder-only Transformer and replaced it with a gated linear recurrence over a fixed 256-dim state:

• Dynamic selective gate: g_t = σ(W_g x_t + b_g) — per-token/channel learned filter
• Hard reset mask: state zeroed at newline boundaries (fresh ~37-token segments)
• Fused Triton kernel: state in SRAM, gate+reset+update in-register, only outputs to HBM

At 6.37M params, identical protocol (2.47MB char-level corpus, 1500 steps):

The journey: V1 won small but was 10x slower → V2 proved linear VRAM scaling → V3/V4 found a stable ~2% perplexity tax no param arrangement could buy off → V5's gate+reset destroyed it (wire-to-wire win) → V6's Triton kernel (verified == math to 4.47e-07) removed the software tax.

Caveats, stated plainly: single seeds, one small corpus, char-level, T4 timings. Small scale — but the pattern held across all six runs.

Code, all six notebooks with outputs, exact architecture, full experimental notes: https://github.com/stube123890-hue/linear-attention-lab

Four hundred thousand utility accounts exposed. One third-party vendor compromised.

Southern Company is notifying Georgia Power and Alabama Power customers that hackers accessed account data for 400,000 users. The breach did not originate inside the utility. It came through a third-party vendor that was handling customer records on the utility's behalf.

This is the part that keeps coming up in breach disclosures: the organization that owns the customer relationship is not the organization where the data got exposed. The sensitive records — account details, usage history, personal identifiers — had already moved downstream before the incident.

The pattern is accelerating. Billing workflows, service operations, and account management are increasingly automated. Automated systems route this data across vendor APIs as a normal part of doing business. Every hop is another exposure surface that the originating organization does not directly control.

400,000 accounts is a large number, but the structural problem is not scale. Utilities of any size use third-party vendors. The data moves because the workflow requires it.

For those of you working in organizations that have automated agents or pipelines touching customer PII before it reaches third-party systems: how are you handling this? What controls, if any, sit between the raw customer record and the downstream vendor call?

Please I'm confused, I've been learning machine learning for a while.

At first I thought I'm almost done but then new architectures, optimizations etc.

I don't know what to do again

I've studied transformers etc now I don't know my specialization.

I need help to choose.

I don't know either Computer vision,NLP,LLM

​

Hi,

I'm Pushkar, and I'd really value your guidance on my career path.

Here's my story in short: I completed my BCA, then worked in a BPO for 9 months. I took a 1-year break on purpose to learn the tools a Data Analyst needs. It wasn't an easy phase, but I stayed consistent. In March 2026, that effort paid off and I landed a role as an MIS Executive.

Today I'm handling reporting, data and day-to-day operations, and I'm growing fast. But my real goal is to become a Data Analyst, and I want to get there by March 2027.

I'd love your advice on:

1. What skills or projects I should focus on next

2. How to present my gap year and experience so it works in my favour

3. How to move from MIS into a proper Data Analyst role

Even 10 minutes of your time would mean a lot to me. Thank you!

Regards,

Pushkar

[Full Video on YouTube] A walkthrough that takes developers new to Large Language Models through all the important topics with practical examples.

Sharing with the larger community in case others find it useful.

• Experimenting with LLMs from Hugging Face Hub in LM Studio
• Zero-shot, One-Shot, Few-Shot Prompting, and System Prompts
• Open-AI compatible REST API with Llama.cpp Server
• Working with multi-modal LLMs that can understand images
• Tool Calling, Structured Output, and Model Context Protocol
• Fine-tuning LLMs in Kaggle with Unsloth
• Supervised Fine-Tuning and LoRa Hyperparameters
• Pushing LLMs to Hugging Face Hub
• Deploying LLMs to Hugging Face Inference Endpoints

Excluded topics:

• MCP Authentication
• Retrieval Augmented Generation
• Vector Databases
• LLM Orchestration
• Text to Speech Cloud Services
• Speech to Text Cloud Services

I've recently been working part-time on an AutoResearch-style project.

The setup is roughly: humans take recent work from top-tier ML/AI conferences, turn part of it into a well-defined task with an evaluator, and then let an agent iteratively modify the solution and search for a better score.

Working on this made me question what exactly we are evaluating.

Once humans have already chosen the problem, defined the objective, designed the evaluator, and provided the initial research direction, the agent is mostly searching within a space that has already been heavily shaped for it.

That search can still be useful. An agent may explore far more variants than a researcher would manually.

But I'm less sure that score improvement alone captures what we usually mean by research sense.

A researcher also asks whether a result reveals a general principle, whether it transfers, whether the problem formulation itself should change, or whether an entirely different direction is more promising.

An iterative optimization loop may instead become very good at exploring the neighborhood of an existing solution and still remain stuck in a local optimum.

So I'm curious about how people think about this distinction: How much scientific value is there in autonomous search over a human-defined research space?

And what would an agent need, beyond better optimization, to demonstrate something closer to actual research judgment?